The Need for Guidance on Integrating SHM Within Military Aircraft Systems
Since February 2009, an international Aerospace Industry Steering Committee (AISC) has been convened to develop guidelines for validating, qualifying and certifying SHM systems. Working within the G-11 division of SAE International, AISC has compiled guidelines for civil transport aircraft. Some of these guidelines can be used for military applications. However, military guidelines are needed to address specific military considerations including concept of operations. The military guidelines should also cover the wider spectrum of military aircraft types and should focus on the key elements required for integrating SHM within military systems. Therefore, a G-11 AISC Military Aircraft Working Group (MAWG) was formed to develop such guidelines. This paper describes the motivation, rationale, scope, milestones and initial work of MAWG. The results of the guidelines will form the future framework for the military community.
Industrial Age NDE to Information Age SHM
The United States Air Force (USAF) is transforming its maintenance practices from the Industrial Age to the Information Age by exploiting opportunities and benefits offered by Information Age technology and techniques. The USAF currently manages its aircraft using a schedule-based maintenance philosophy. This schedule-based approach works well for ensuring aircraft integrity; however, it is very costly, labor-intensive, and reduces aircraft availability. Structural Health Monitoring (SHM) systems have the potential to analyze near-real-time and historical weapon systems data to provide a predictive maintenance capability. However, most research in SHM is focused on in-situ structural inspection techniques instead of structural monitoring. Structural inspections typically entail examining key locations of the airframe for material degradation or flaws. These examinations usually occur at predefined intervals between extended periods of time with each inspection considered an independent evaluation. Conversely, structural monitoring involves continuous condition surveillance of an airframe over an extended period of time. It uses past conditions and expected future conditions for producing a comprehensive understanding of the current health state. In this paper, the full potential impact SHM could have on airframe management is examined. As demonstrated in a laboratory experiment, SHM has the potential to improve effectiveness and efficiency metrics compared to the current USAF maintenance procedures.
On the Search for Optimal Damage Precursors
A new approach to predict the service life of critical components via study of damage precursors is emerging and is the topic of this paper. To date most service life predictions are based on measurements of damage indicators and their growth towards criticality or failure, e.g. fatigue crack length, material loss due to corrosion or wear, etc. For many cases of concern, especially fatigue, the critical, direct damage indicator, Di, i.e. crack length, increases exponentially towards criticality, roughly dDi/dN α Di n where n can exceed 4. This makes lifetime estimates based on measurements of damage, e.g. around half-life, or even at 80 percent life, difficult and inaccurate, even for cases where the damage progress is well modeled and understood. To improve the accuracy and reliability of lifetime prediction, efforts are now underway to determine the state awareness of a critical component during service, based on property characterizations, in addition to the measurements of the direct damage indicators, such as crack length, acoustic emission, ultrasound signals, eddy current measurements, etc. These characterizations will include indirect damage indicators, i.e. precursors, and allied or affiliated damage indicators. Precursors, e.g. to fatigue crack formation, include electrical resistivity changes, dislocation distribution changes and crazing. For affiliated damage indicators residual stress relaxation or development, phase changes, electrical property (resistivity, dielectric constant, permeability), microstructural characterization, must be considered. The selection of the optimal combination of direct and indirect damage indicators will be application specific. For example for ultra-high strength steels and aluminum alloys, with high damage exponents n, where the critical crack length may also be very small and difficult to detect, measurements of reliable precursors and indirect damage indicators could significantly improve service life prediction. It is proposed to assess the efficacy of damage indicators on the basis of their Di/Df vs. Ni/Nf, i.e. damage ratio vs. life fraction curves, (referred to as damage indicator ratio curves, or DIR curves) searching for indicators with DIR curves that best meet the needs of the application.
A Vision on Stretchable Bio-Inspired Networks for Intelligent Structures
Major progress has been made recently in structural health monitoring maturing the technology through quantification, validation and verification to promote implementation and fielding of SHM Systems, which are key activities in this workshop. In addition there is a lot of work seeking to detect damage precursors and to deploy SHM systems over large areas, moving the technology beyond hot-spot monitoring to global state sensing for full structural coverage. A large amount of small sensors of multiple types are necessary in order to accomplish this, enabling increased sensing capabilities while reducing parasitic effects on host structures. Traditional sensors are large and heavy, adding to the weight of a structure and requiring physical accommodation without adding to, and potentially degrading the strength of the overall structure. Increased numbers of sensors must also be deployed to span large areas while maintaining or increasing sensing resolution and quantification capabilities. These sensors are typically assembled, wired, and installed individually, by hand, making mass deployment prohibitively time consuming and expensive. In order to overcome these limitations the Structures and Composites Lab at Stanford University has worked to develop bio-inspired microfabricated stretchable sensor networks. Adopting the concept of C-MOS and MEMS fabrication techniques, new methods are being developed to completely integrate networks of large numbers of various micro-scale sensors, processors, switches and all wiring in a single fabrication process. Then the networks are stretched to span areas orders of magnitude larger than the original fabrication area and deployed onto host structures. The small scale components enables interlaminar installation in laminar composites or adhesive layers of built up structures while simultaneously minimizing parasitic effects on the host structure. Additionally, data processing and interpretation capabilities could be embedded into the network before material integration to make the material truly multifunctional and intelligent once fully deployed. This paper reviews the current accomplishment and future vision for these systems in the pursuit of state sensing and intelligent materials for self diagnostics and health monitoring.
Robust Sensors for Structural Health Monitoring within Harsh Environments
Structural health monitoring of components that operate within harsh environments requires the development of advanced and robust sensor technology. More specifically, small-scale sensors that can operate and transduce electrical signals at elevated temperatures and chemically corrosive environments for extended periods. The realization of structural health monitoring technology for harsh environment applications could enable prevention of critical component failure leading to increased operation lifetimes, increased safety and reduced operation costs. This paper introduces the need for harsh environment sensing technology, summarizes the stateof- the-art technology and provides recommendations for future development of harsh environment sensors.
Damage Detection at an Aluminum Beam from Discrete and Continuous Strain Measurements
Unless the sensors are closely located to a local defect, the change in the global strain field caused by the defect is very small, and may go faded by other environmental effects. Only when compared the strain readings at many points, some information about damage may be unveiled. Robust automated techniques are needed to do this comparison. Principal Component Analysis (PCA) is a well-known statistical technique that has been used as a pattern recognition technique by several years with excellent results. It allows obtaining pattern that often underlie from the data by calculating the principal components and re-expressing the information in a new space. Damage index are already available. An experimental validation of the technique is discussed in this paper, comparing damages of different sizes and positions, under a set of combined loads, both under static and dynamic conditions. Strains were measured at several points by bonded FBGs (Fiber Bragg Gratings), and also along continuous lines by optical fiber distributed sensing (OBR, Optical Backscatter Reflectometer). The sensitivity of the approach and the influence of parameters (number of sensors, distance to the damage) are quantified.
Detecting and Locating Fatigue Cracks in a Complex Wing-Box Structure Using the Acoustic Emission Technique: A Verification Study
Acoustic Emission (AE) is recognized for its potential as a Structural Health Monitoring (SHM) technique, offering several advantages, such as passive sensing and wide area monitoring, making it particularly attractive for aerospace applications. Realistic structures, like aircraft wings for example, consist of structural elements of complex geometry, manufactured with different materials. Individually these elements may influence the performance of the SHM technique in detecting and locating structural damage. In this research, the capabilities of the AE technique to identify and locate damage in complex structures were assessed. These assessments were performed without disclosure of the damage location to the AE equipment operator. Four piezoelectric AE sensors were mounted on spars of the aircraft wing representative structure prior to conducting a series of constant amplitude loading fatigue tests. One of the spars contained a fatigue crack seeded at a location unknown to the damage monitoring analyst. The sensors monitored AE generated from the spars and event location estimates were obtained using time of flight analysis with a wave velocity value determined experimentally. The AE monitoring successful detected and located the hidden fatigue crack; this was independently verified by three nondestructive testing (NDT) methods.
Oriented Wireless Sensing for Structural Health Monitoring
A novel sensor platform for Structural Health Monitoring is described and explored: a small, energy-efficient wireless sensor platform based on MEMS sensors, a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer. The platform allows for measurements novel for SHM applications: orientation angles in reference to a global coordinate system (pitch, roll, yaw), used either directly or as part of a direct calculation of displacement in absolute space. Hardware for platform is delineated; software concerns are explored, and several filters for sensor fusion are demonstrated. Applications for SHM are detailed, both dynamic and static applications. The framework for a validation study for the platform is described; multiple tests (inplane/out-of-plane) are performed on a shake-table, for ground motions used to demonstrate validity of platform for practical application.
Smart, Tough and Successful: Three New Innovative Approaches for Diagnosis and Prognosis of Technical Systems
Ensuring the total reliability and availability complex systems such as safe mechatronic systems or cost-sensitive machine components is of increasing importance. Besides the availability of task specific sensors and filtering techniques two major issues are of relevance: i) Preparation of the measured data (filtering) and ii) Interpretation of the data with respect to the machines state as well as to the machines remaining lifetime or guaranteed functionality. Core of this contribution is the development and first application of simple, easy to use, easy to apply, and easy to handle algorithms to be used directly with industrial data or measurements from technical systems in operation. The three approaches applied are: AI Acoustic Emission (AE) in combination using a suitable filter to be designed AII Data analysis using macro data feature capturing technique AIII Adaptive fuzzy-based filtering [1] with training and classification modules. Experimental data are used. The approaches use different algorithms as well as different signals (from same experiment) and show more or less the same results but allowing a different and distinguished problem-oriented insight to the complex wear process of the system.
A Novel Approach for Quantitative Assessment of Delamination Damage Based on Vibrational Response
Existing vibration-based approaches for damage detection are qualitative, providing indications of possible existence of damage. This paper presents a novel two-step approach for quantifying laminar damage, such as delamination in composites and thickness reduction in metallic structures due to corrosion damage. This new approach first employs a gap smoothing method to determine the location from curvature data from vibrational response. The severity of damage is then determined in the second step using an inverse method by matching predictions of finite-element analysis with deflection (or curvature) data. An assessment of various approaches for computing curvature reveals that wavelet transform is a promising method for simultaneously removing noise and computing curvature. Comparisons with experimental results show that this new two-step approach is capable of determining the size and severity of structural damage, which can be used as input for a residual strength assessment.
Detection of Instability for Civil Large-Scale Space Structures
A number of structural collapses were caused by the loss of stability. However, the monitoring and detection of instability is rarely studied in both communities of structural health monitoring and structural stability. In this paper, a novel yet practical approach to detecting instability of civil large-scale space structures will be proposed. The basic idea of this approach is: when buckling initiates, the load-bearing characteristics of a member in compression change. The internal force in this member changes from an axial compressive force to a bending moment and shear force. And accordingly, the stress/strain distribution becomes variant along the member due to the fact that the bending moment along the member is different, while the stress/strain along the member remains the same before the buckling happens. By identifying the difference in strain at several different locations in the member, the buckling can be detected. In this study, a large-scale reticulated shell structure is considered to validate the proposed approach. A 3-D finite element model of this structure is developed and the process of losing stability is simulated using ANSYS. The numerical simulation results have demonstrated the efficacy of the proposed approach.
Crack Detection in Metallic Plate Elements Using a Point-Based Thermal Measurement Strategy
This paper presents a method of thermography-based damage detection that employs an economic sensor setup that is feasible for permanent deployment in operational structures. The setup includes a joule (resistive) heating element and an array of IC temperature sensors mounted to the boundary of a metallic plate. As the heat is conducted from the resistor through the metal, sensors measure the time variance in temperature rise at the sensor locations. This data can then be used in an inverse manner using a high-fidelity finite element model (FEM) to solve for imperfections in the metal that cause delays in heat conduction. Aluminum plate specimens with and without damage are used. Sensor data is collected via wireless sensing nodes. Correlations between the finite element and the IC temperature sensor system data are analyzed.
Detection, Localization and Quantification of Anomalies in Mass, Stiffness and Damping Based on Time Series Modelling Using Output-Only Data
In this paper, an improved time series approach for anomaly detection, localization and quantification using output-only data is presented. It was shown in previous studies conducted by the authors that this methodology was able to detect, locate and quantify the changes in the mass, damping and stiffness parameters in a numerical model for noise-free conditions accurately. In this paper, the approach is extended for damage identification when artificial noise is added to the data. This approach is mainly based on the idea that the changes in the structural properties of an infrastructure can be related to the change in free vibration response (displacement, velocity and acceleration) of the structure. In this approach, the sensors of a structure are first categorized into different clusters according to their locations. Then, ARX models (Auto-Regressive models with eXogenous input), where only output data under free vibration are used, are created for different sensor clusters. Building these ARX models for baseline and damaged structures, the location and degree of the anomaly can be identified on the basis of the changes in the coefficients in the models. In order to verify the approach, it is applied to a numerical structural model representing a laboratory specimen. The results show that the change of mass, stiffness and damping for noisy conditions can be identified separately by using this approach even if these changes occur simultaneously.
Detection and Quantification of a Disbonded Aluminum Honeycomb Panel Using Nonlinear Superharmonic Frequencies
Damage in composite materials is challenging to detect because of complex behavior. Using nonlinear analytical techniques, the behavior of a disbond in an aluminum honeycomb material is illuminated. By analyzing the superharmonic frequencies under sinusoidal excitation, quadratic stiffness was seen in a disbonded panel and tracked as the damage area increased. A cubic nonlinearity was also observed, but only when the damaged area was larger. It is hypothesized that the quadratic nonlinearity is due to the change in face sheet stiffness as the top sheet vibrates off and into the honeycomb core. The cubic nonlinearity is not as easily explained due to the changes in response amplitude not following the analytical approximations. The lack of a nonlinear backbone curve indicates that the cubic nonlinearity is not related to stiffness but most likely damping. The elastic behavior of the epoxy adhesive under high strains and geometric constraints is thought to contribute to the cubic nonlinearity. More work into understanding the relationship between the epoxy viscoelastic behavior and the panel response is necessary.
Bayesian Inference Based Model Calibration for the Dynamic Analysis of Seismic Isolated Bridges
The model validation has a key role to obtain appropriate numerical analysis outputs that are used for the seismic strengthening or retrofit design of existing bridges in Japan. In this study, we verified the applicability of the Bayesian inference based model calibration for the numerical modeling of existing bridges especially focusing on the validity of boundary conditions, which were given by the bridge bearings. The target structure was an existing seismic isolated bridge with laminated rubber bearings; this bearing sometimes shows uncertain changes in the elastic property caused by the residual deformations due to previous earthquakes. The prior distributions of calibration parameters, i.e., material properties and spring stiffness for the rubber bearings in the FE model of the target bridge, were then updated to the Bayesian posterior distributions using the resonant frequencies acquired by the impact tests. The calibration model was obtained by assigning the mean values of the posterior distributions. The resonant frequencies of calibrated model then showed successful improvement in the agreement with the experimental result. Moreover, the possibility of identifying the uncertain changes of bearing parameters was also investigated by the experimental verification using a beam structure. The uncertainties of the rubber bearing parameters of the numerical model were successfully reduced, and furthermore, the change of parameter due to the residual deformation could be identified from obtained Bayesian posterior distributions.
Curing Strength Monitoring of Early-Age Concrete Using Embedded Piezoelectric Sensors for Ubiquitous Construction Process Management
In the construction using the concrete, it’s strongly required to evaluate the strength development during the curing process to ensure the good quality of concrete. Especially, monitoring the curing strength at early-age is very important to reduce the construction cost and time because it can provide the accurate information for the decision to progress to the next process safely. In this context, a high frequency dynamic response based nondestructive curing strength gain monitoring method that can be utilized even for the early-age concrete structures was proposed. This approach used embedded piezo-electric sensors which were inserted at the same time with concrete placement to measure the high frequency dynamic response signal from the early-age concrete. While the concrete is cured, the guided wave signals from the embedded sensors were measured continuously at a regular interval, and it was observed that the velocity of guided wave is varied according to the concrete curing process. The changing pattern of the wave velocity was compared with the development of the compressive strength from compression test, their hysteresis curves were changed in a similar manner.
Bridge Pier Scour Prediction by Multi-Objective Optimization Using the Genetic Algorithm
Local scour is a critical problem for the safety of bridges. In this study, a new bridge-pier-scour model is proposed by multi-objective optimization using genetic algorithm based on the traditional regression model and the inductive method. A model evaluation function has been established (HEC-18 equation, Froehlich equation, and GEP model). Two groups of field datasets; bridge scour management data base (358 sets) and FHWA (110 sets), are used to evaluate the models. Based on the model evaluation function and statistical evaluation, the new model is shown more efficient and less failed in predicting the scour depth at bridge piers.
Corrosivity Sensor Suite for Measuring Damage Accumulation from Corrosion in Aircraft Applications
Structural integrity calculations are used to determine the lifetime of Defense aircraft structures, by predicting fatigue damage given a starting crack size. Presently, the effect of damage accumulation from corrosion is unaccounted for in these calculations. Corrosion is known to accelerate both crack nucleation and propagation, and without this knowledge of corrosion damage, structural integrity calculations could underestimate the potential lifetime of aircraft. Although a number of advancements have been made to establish diagnostic and prognostic systems for monitoring the corrosion of alloys used in aircraft, presently there are no viable continuous monitoring techniques that can provide direct, high resolution measurements of corrosion over large areas. Direct localized measurements of corrosion and environmental parameters, however, would improve fatigue management of aircraft by more accurately predicting the damage state and enabling more efficient condition based interval maintenance, replacing costly schedule based maintenance and inspection practices. A wireless corrosion monitoring system that can be retrofit into existing aircraft structures has been developed that combines localized measurements of corrosion and environmental parameters that contribute to corrosion with knowledge of aircraft usage to estimate current and future damage to aircraft. Numerous studies have linked environmental parameters, such as relative humidity and chloride concentration, to the corrosion of aircraft alloys. Sensors that measure these environmental parameters, including surface and air temperature, relative humidity, solution resistance, and polarization resistance have been integrated within a compact, robust package. The present development effort focused on establishing this sensing platform to measure environmental parameters, corrosion rates, and cumulative corrosion, and the application of these measurements to classify corrosivity and the structural health of aircraft are presented.
Statistical Based Decision Making for Damage Localization with Influence Lines
A theorem on damage localization from flexibility changes has been proven recently, where it has been shown that the image of the change in flexibility dF between damaged and reference states of a structure is a basis for the influence lines of stress resultants at the damaged locations. This damage localization approach can operate on output-only vibration measurements from damaged and reference states, and a finite element model of the structure in reference state is required. While the localization approach is based on purely mechanical principles, an estimate of the image of dF is required from the data that is subject to statistical uncertainty due to unknown noise excitation and finite data length. In this paper, this uncertainty is quantified from the measurements and a statistical framework is added for the decision about damaged elements. The combined approach is successfully applied to a numerical simulation and to a cantilever beam in a lab experiment.
Characterisation of Part-Through Damage in a Stiffened Panel by a Time-Reversal Based Imaging Method
Most of the research on the use of guided wave to detect and quantify damage, particularly those based on time-reversal concept and diffraction tomography, requires analytical solution of the Green’s function. This paper presents an extended time-reversal imaging approach which enables the use of numerically determined Green’s function in imaging of a structure with complex geometry. Imaging results have revealed that damage location, shape and size can be accurately constructed if the scatter wave field for a stiffened plate is sampled in the spatial domain by 16 or more sensors.
Distortion Index for Assessment of Damage Growth in a Composite Structures Using Spectral Distortion of Embedded FBG Sensors
Structural Health Monitoring Systems based on embedded FBG sensors, to identify damage conditions, are largely dependent on the spectral distortion of the sensors. The uneven stress gradient occurring along the grating of FBG sensors, due to damage inside composite structures can be estimated by analyzing significant changes that appear in the FBG response spectra. However, the stochastic nature of the distorted shape of the FBG spectra makes it difficult to interpret and quantify the existing damage at the location of the FBG sensors. This research works on a novel concept of the “Distortion Index (DI)â€ which is defined using distorted spectra of FBG sensors. It was observed that the DI increases with the increase in damage size.
Supervised and Unsupervised Machine Learning Approaches for Bridge Damage Prediction
Structural health monitoring (SHM) starts to gain increasing attentions in the recent decade with the enormous support received from information and communication technology (ICT) which provides not only efficient data acquisition and transmission instruments, but also data analysis techniques for system modeling. In this work, we present a novel approach for bridge health assessment and damage localization based on vibration monitoring. Specifically, we consider one of the largest bridges in Sydney as a study case. We aim to distinguish damaged and undamaged joints in the bridge via supervised and unsupervised machine learning methods. Health conditions of bridge joints are reflected by their vibrations caused by vehicle passing events. The proposed machine learning methods train classifiers on historical vibration data for the purpose of distinguishing vibrations from healthy and unhealthy joints. Vibration events are recorded by 18 tri-axial accelerometers, 3 per joint with a total of 6 joints. Attempts have been made to normalize input data by selecting events with the dominant occurrence order of vibration peaks. Both time domain and frequency domain features have been used for training and testing. 10-fold cross validation is used for evaluation. For supervised method, 99.71% and 99.30% detection accuracies are achieved for time and frequency domain features respectively. Peak selection boosts the detection accuracy from 69% to 84% for the unsupervised method.
Damage Characterization by Model Updating Techniques Using a Derivative-Free Global Optimization Method
This work presents a damage characterization approach for composite structures, based on a global optimization technique, combining data retrieved from a finite element method, FEM, model and experimental signals from sensors (e.g., accelerometers). Damage diagnosis is based on vibrational methods. The proposed approach is able of simultaneously locating, determining the type of damage (changes on the stiffness, mass, damping) and outputting its severity (size and variation in material properties). The method demands automated pre/post-processing operations of dynamic FEM models, using Abaqus® scripting capabilities and continuous data exchange between Abaqus® and the external global optimization module based on Particle Swarm method. This is implemented into PRODDIA® – Structural Systems Health Management platform that manages the data and information flows. The method is firstly validated by a simulation approach and then an experimental case study is presented, validating its appropriateness and effectiveness to damage assessment on composite structures.
High Resolution Localization with Lamb Wave Sparse Wavenumber Analysis
Guided wave structural health monitoring techniques have grown in popularity due to their ability to interrogate large areas at once and their sensitivity to damage in structures. However, guided waves are inherently complex due to their dispersive and multi-modal characteristics. These characteristics also change with variations in environmental conditions. As a result, many sophisticated localization algorithms, which rely on precise knowledge of the medium, fail to successfully locate damage. Alternatively, many current localization approaches preprocess data to simplify the measurements and reduce the adverse effects of dispersion and multiple modes. Often, these approaches only consider the first arriving wave mode across a narrow band of frequencies. They also reduce the effects of dispersion by analyzing the envelope of the received signals rather than the raw data. While these preprocessing steps may help to improve localization accuracy, they significantly degrade the resulting resolution and image quality. In this paper, we integrate a methodology known as sparse wavenumber analysis with current localization algorithms to utilize the multiple modes and dispersive characteristics of Lamb waves in a plate across a wide band of frequencies to localize damage without computing envelopes or performing similar preprocessing steps.
Damage Location of Civil Large-Scale Space Structures Based on Average Axial-Strain Mode Shapes
Civil large-scale space structures are usually built for venues where hundreds of people assemble, such as sports stadiums/arenas, gymnasiums, and others. Failure of this type of structure may endanger the safety of hundreds of people. Although structural health monitoring (SHM) and damage detection of a wide variety of structures (such as bridges, buildings, off-shore platforms, and dams) have been extensively studied, SHM of civil large-scale space structures is rarely studied due to their complexity. An effective damage detection approach for this type of structure would increase the integrity of the structure and the safety of the general public. This study proposes a novel approach to accurately localize damaged members without using the baseline data. First, average axial-strain (AAS) mode shapes are defined. AAS mode shapes are associated with average axial-strain of members, and they are extracted from translational mode shapes. Then, a damage indicator based on AAS mode shapes is proposed. This approach does not require baseline data, which circumvents the difficulty in matching mode shapes before and after damage, specifically exist in this type of structure. This approach has been validated by numerical simulation of a large-scale reticulated shell structure.
Baseline-Free Estimation of the Residual Fatigue Life Using a Modulated Nonlinear Ultrasound Method
This work develops a modulated nonlinear elastic wave spectroscopy (NEWS) method for the evaluation of a metallic components residual fatigue life. An aluminium specimen (AA6082-T6) was tested at predetermined points throughout its fatigue life using a dual-frequency ultrasound method. A modulated nonlinear parameter was derived, which described the relationship between the generation of modulated (sideband) responses of a dual frequency signal and the linear response. The sideband generation from the dual frequency was shown to increase as the residual fatigue life decreased, and as a standalone measurement method it can be used to show an increase in a materials damage. Although analytical evaluation of the method was required to develop a baseline-free method, work done by Meo and Amerini combined the Paris law and the Nazarov-Sutin crack equation developing the theoretical model. The results showed good correlation between the derived theoretical model and the modulated nonlinear parameter, allowing for material residual fatigue life estimation.
Performance Comparison of Different Autoregressive Damage Features Using Acceleration Measurements from a Truss Bridge
Time series analysis has been applied to structural monitoring signals for system damage identification in a number of research literatures. Among various time series analysis tools, univariate autoregressive modeling (AR) is one of the most commonly used methods because of its innate computational efficiency. In this paper, three autoregressive damage features extracted directly from the ambient vibration data and from the vibration signal autocorrelation will be presented. Two of the features are distance functions of AR model parameters and the third feature is a function of AR residuals. These features are then applied to acceleration measurements collected from a member of a truss bridge to detect a structural change, and their performances are compared and commented.
Distinguishing Between Sensor Performance Degradation and Structural Damage Using Generalized Likelihood Ratio Test (GLRT) and Statistical Process Control
This paper proposes a method which can distinguish between sensor performance degradation and structural damage in a vibration-based structural health monitoring system. This method utilizes the fact that the sensor performance degradation is local, while structural damage is global. In this paper, the generalized likelihood ratio test (GLRT) is firstly used to detect and localize a change in the system; and then the percentage of the extreme value of the largest principal component scores of the generalized likelihood is calculated, and finally a quality control chart is obtained which can be utilized to distinguish between sensor performance degradation and structural damage. On the other hand, if the source of the novelty is from sensor degradation, the extreme values are out of the bounds; else the extreme values are not out of the bounds. A simply-supported steel beam is experimentally performed to validate the proposed method. The results show that the proposed method can effectively detect the abnormal response and reduce the possibility of misclassifying sensor performance degradation and damage, as well as false alarming to some extent.
Application of Local Defect Resonance Method to Structural Damage Detection
Barely visible impact damage in a layered composite plate is investigated. Two experimental approaches, based on nonlinear acoustics, are applied for impact damage detection. The Local Defect Resonance method and the Second Harmonic Identification Technique are used to reveal the damage. A low-profile piezoceramic stack actuator is applied to excite the plate and a scanning laser vibrometer is used to acquire response signals. The effectiveness of damage imaging and practical aspects of applying the presented techniques are demonstrated and discussed.
Auto-Association and Novelty Detection: Truths and Myths?
In many damage detection approaches it is often impossible, or very difficult, to obtain true measurements for all possible damage classes, especially in complex structures. In fact, data that is collected during any damaged state of a structure is usually very rare. This is the reason that unsupervised learning is critical. For databased approaches to structural health monitoring the premise of novelty detection techniques is to seek the answer to a simple question; given a newly presented measurement from the structure, does one believe it to have come from the structure in its undamaged state? Auto-associative neural networks consisting of five layers have been used as an advanced method for novelty detection in the past. In this study an analysis is performed in order to demonstrate the ability of nonlinear autoassociators with three layers for multimodal classification problems and novelty detection. Also, another critical issue, that of generalisation in the context of autoassociators is investigated and discussed. Is generalisation needed for novelty detection when neural networks are used or not?
Laser-Based Automatic Cross-Sectional Change Detection for Steel Frames
The aging infrastructure in the U.S. poses a major challenge for society since thousands of registered dams, bridges and other infrastructure components are now operating for more than five decades and have damage due to age or accumulated damage from hazards. Assessing present structural safety condition of these structures is vital for the process of evaluating their current status and deciding on future steps for rehabilitation. The in situ condition of structures can be captured by laser scanners that create dense 3D point clouds. However, deducting meaningful information from point clouds is generally difficult, and most of the time user-interaction is mandatory. Furthermore, it is even more challenging to automate this process and to detect deteriorated and damaged locations on the structure through point cloud processing. This requires dividing the point cloud into meaningful clusters obtained through segmentation and relationship modeling, followed by object recognition. It is then possible to locate defects on the structure through comparison once the objects from model library are correctly detected and fitted into the captured point cloud. This paper presents a new approach for detecting defects due to material loss through point cloud processing by using object detection and model comparison.
Detection of Impact Damage Using a Nonlinear Ultrasound Approach
Developments in carbon fibres reinforced plastic (CFRP) materials have allowed a radical advancement in lightweight aerospace applications. However, these components are sensitive to low velocity impact damages that can considerably degrade the structural integrity and, if not detected, they can result in loss of control and catastrophic failures. Literature provides a quantitative number of linear and nonlinear diagnostic imaging methods that can continuously provide a detailed image of the structural damage. This paper presents a nonlinear Structural Health Monitoring (SHM) imaging method, based on nonlinear ultrasonic guided waves (GW), for the detection of the delaminations in a CFRP composite structure caused by low velocity impact loadings. In particular, this research study images the damage by using a second order nonlinear signature. The proposed technique, based on a combination of second order phase symmetry analysis (PSA) with chirp excitation and inverse filtering (IF), is applied to a number of waveforms containing the nonlinear impulse responses of the medium. Phase symmetry analysis was used to characterise the second order nonlinearity of the structure due to delamination, by exploiting its invariant properties with the phase angle of the pulse compressed chirp signals. Then, the IF approach was applied to a number of waveforms stored into a database containing the experimental second order transfer function of the structure. Unlike other ultrasonic imaging methods, the present technique allows achieving the optimal focalization of the nonlinear source in the spatial and time domain, by taking advantage of multiple linear scattering and a small number of receiver sensors. In addition, this methodology does not require any baseline with the undamaged structure as well as iterative algorithms for the detection of the nonlinear source. The proposed method was compared to classical C-scan showing a robust and accurate system to image impact damage in composites structures.
An Application of Near Infra-Red Fibre Bragg Grating as Dynamic Sensor in SHM of Thin Composite Laminates
Vibration testing is an essential component in Structural Health Monitoring (SHM). It can provide vital information regarding the integrity of critical structure; for instance, it can provide information on progressive failure monitoring of composites structure in the aerospace industry. Over the past decade, there have been many successful researches showing extraordinary ability of Fiber Bragg grating (FBG) sensors as a dynamic sensor. Ability of acquiring both static and dynamic strain measurements, make FBG sensor as a good alternative to replace the conventional vibration sensors. In addition the physical size of FBG sensor provides greater access to embed them in composite structures without affecting to any properties of the composite. However, in most applications to date, people have used only the FBG with wavelength 1550 nm. Moreover, FBG sensors with this wavelength are commonly use in industries such as telecommunications and medical industries. However, there is an option of using near infra-red (NIR) FBG range which comparably cheap in term of total system design. This paper details the use of near infra-red (NIR) FBGs as dynamic sensors; a part of SHM system for the monitoring of the damages in a thin glass fiber composite plates. Results reveal that the NIR FBG range gives good response to an impact and; also to applied high frequency vibrations.
A Novel Reduced Model for Damage Detection of Frame Structures
When performing damage detection for frame buildings, a shear structure model is frequently employed to model the dynamic responses of the frame. The estimated parameters of the shear model are then utilized to determine the damage status of the frame. However, it was pointed out in many studies that, due to the existence of modeling error, the shear model cannot very well represent the true dynamic behavior of the frame; hence, the damage detection results are often inaccurate. In this paper, a novel reduced model of frame structures is proposed for the damage detection of frame structures, which has two important characteristics: 1) compared with shear model, the new model can reflect the true interstory shear forces of the frame buildings, thereby significantly reducing the modeling error and improving damage detection accuracy; 2) the parameters of the new model have a clear relation with the physical parameters of the frame, facilitating using the estimated parameters of the new model to determine the damage status of the frame. Moreover, based on this new model, an iterative substructure identification method is proposed to identify the parameters of frame structures. Finally, a numerical example of a frame structure is utilized to demonstrate the effectiveness of the new model. The results show that using the new model greatly improve the accuracy of the damage detection of the frame structure, compared with using a shear structure model.
Development and Validation of a Tree-Ring Bio-Inspired Wireless Fatigue Monitoring System
Fatigue, which may cause sudden failure of the critical structures, has been a key problem for the aircrafts and railway systems for centuries. With uncertain loading conditions, the fatigue life of the involved structure is inherently random and few sensing techniques are reliable for long-term monitoring of fatigue damage. This paper proposes a novel intelligent system targeted at fatigue damage monitoring of in-situ structures with online structural health monitoring. The developed intelligent system combines the concepts of tree-ring bio-inspired data track technique with the real-time wireless structural health monitoring (SHM) sensing technique. A general framework, the methodology, and the strategy of the intelligent fatigue monitoring system are investigated in details. The rain-flow counting method is adopted as the core algorithm for determining the fatigue damages from detected structural behavior parameters under various loading conditions. The employment of the Digital Signal Processing (DSP) is then followed as the core data processing module to collect and analyzed the monitored fatigue damages. Laboratory test results show that the developed intelligent fatigue monitoring system can provide a reliable and quick feedback and warning of fatigue failure. Therefore, with the advantages of low cost, ease for handling and installation, simple concept, high accuracy, and great reliability, the proposed tree-ring bio-inspired wireless fatigue monitoring system can be further applied to practical applications such as in mechanical engineering, civil infrastructures, transportation systems, aerospace engineering, and energy related structures.
Comparative Assessment of Static and Dynamic Damage Identification Indices Based on Distributed Macro-Strain Responses
In this paper, a new method for damage assessment of reinforced concrete (RC) beams is presented. This method is based on static data measured with distributed long-gage macro-strain sensors. Based on static macro-strain, the static damage index independent of load is theoretically deducted considering the variations of neutral axis height and flexural rigidity. RC beam experiment was conducted to verify the effectiveness of the proposed method. The experimental results of a RC beam show that small crack initiation and propagation gradually induced by means of static load testing can also be identified. Meanwhile, damage indices using static and dynamic responses are compared and both of them are effective for damage identification of the beam structure. Therefore, distributed long-gage macro-strain sensing technique has great capacity for the damage assessment of the reinforced concrete structures.
Development of Self-Compensated Long-Gage Carbon Sensor for Structural Health Monitoring
A self-compensated long-gage packaging method of carbon fiber sensor has been developed for solving thermoelectric and moisture influence for assessing the continuous measuring of low-level strains. The continuous measuring of low-level strains is faced with the burden of a complex variety of excitations and an unstable environmental effect. Carbon fiber sensors are ideally suited for static and dynamic strain measurement at low strain levels. However, continuous carbon fibers used as the strain sensing elements in the carbon fiber sensor exhibited an obviously thermoelectric effect. Thus, to reduce the thermally induced resistance change of the carbon sensor to an acceptable level, a novel self-compensation long-gage packaging method was established using carbon sensors placed in series and packaged with low-cost insulating basalt sheets. Experimental verifications of the developed self-compensated long-gage carbon sensor under dry heating and waterbath heating were present. Good agreement of the measured strain results under different moisture condition has been achieved. The packaging method is good to prevent moisture influence and suitable to build up temperature compensation. Furthermore, a long-gage carbon sensor was installed on a tension specimen to provide the continuous monitoring with static loading and impact excitations. The obtained dynamic response is in agreement with the experimental setting. Therefore, the developed long-gage carbon sensor is useful in the identification of excitations under low strain level. Furthermore, with a continuous wavelet transform-based time-frequency analysis, it can be considered the macro strain time histories and frequency identification using the developed long-gage carbon sensor is substantially accurate.
Implementing the Surface Response to Excitation Method (SuRE) with Non-Contact Sensor at Hard to Reach Locations
Performance of the Surface Response to Excitation (SuRE) method was evaluated when the data was collected with a scanning laser vibrometer while an ordinary mirror direct the beam to the desired location. The surface of a horizontally located test plate was excited with a piezoelectric element. The horizontal beam of the scanning laser vibrometer was directed to the test plate surface by reflecting it of an ordinary mirror. The location of the weight was estimated from the contour maps which show the differences of the transfer functions at the grid location before and after the load. The locations of the weight in four different tests were estimated with reasonable accuracy.
Design and Characterization of Fiber-Optic Interferometric Sensor for Deflection and Damage Detection of Morphing Wing Structures
The paper deals with a non-destructive technique capable for simultaneous damage and deflection detection of aircraft’s morphing wings made as a sandwich structure of CFRP. We proposed a fiber-optic low-coherence interferometry as a sensing platform for deflection and structural health monitoring of aircraft morphing wings in the frame of an EU-FP7 project, named “Fiber Optic System for Deflection and Damage Detection (FOS3D)â€. Fiber-optic sensors (FOS) of different forms are adhesively bonded over the top and down side of the CFRP morphing structure. We performed theoretical and experimental exploration of such a system steaming from the FEM simulation of deformation magnitude in dependence on the inclination angle (±5°) of the morphing tail. Then we calculated the change of the optical path difference (OPD) induced in the FOS in dependence on the inclination angle and found correlation between the OPD of FOS and coherence length of the used superluminiscente diodes (SLD). The CFRP structure and FOS have been simultaneously subjected to the deflection by standard four-point bending test and to damage by “pencil brake testâ€. During loading of the CFRP plate raw interferometric signals have been acquired and then off-line processed by “arctanâ€ algorithm. We determined the most critical parameters of the fiber-optic sensing configuration, which provide information on deflection and damage events raised into the subjected CFRP specimen.
Online Simultaneous Reconstruction of Wind Load and Structural Responses for High-Rise Structures
The actual wind load information is critical for evaluating the health status of highrise structures after experiencing extreme events, e.g. typhoons. However, as a type of distributed load, the wind load is difficult to be measured directly. A possible solution is to reconstruct it from the structural response measurements. This process is often an ill-posed inverse problem. In this paper, a steady-state Kalman filter based unknown input estimator is adopted to solve the above mentioned ill-posed inverse problem by fulfilling some stability criteria. Being translated from mathematics, such stability criteria are actually some requirements on sensor type, sensor number and sensor position. This type of estimator is real-time executable and is capable of estimating the unknown input and the system states within in one sampling time, which indicates the online reconstruction of wind load and structural responses is possible. The 600 meter tall Canton Tower is situated in a typhoon active area, and a structural health monitoring (SHM) system has already been integrated onto this tower. This makes it an ideal testbed for validating the above illustrated wind load reconstruction stragtegy. Measurements obtained during the Typhoon “Nanmadolâ€ and the Typhoon “Kai-takâ€ are analyzed and the reconstruction results are presented.
Sensor Capability Enhancement of PPP-BOTDA for Dynamic Distributed Strain Monitoring of Bridge Structures
This article presents a signal-processing method for developing dynamic strain measurements of using distributed fiber optic sensing technique. To address the dynamic structural responses under random vibrations and shorten the nonlinear measuring error of Brillouin optical time-domain analysis (BOTDA)-based strain measurement, several analysis techniques are compared. The presented method combines the distributed sensing ability of BOTDA-based measurement and the discrete wavelet transform (DWT)-based signal decomposition to construct a low frequency band of time history vector of each spatial arrangement on the measured fiber optic sensor. The time history vector of high-level approximation coefficients of on the strained and unstrained sensing part receive similar systematic measurement errors, which is suitable for integrating a critical model of systematic errors. The critical model is used to index and modify the possible systematic measurement errors on the strained sensing part. Furthermore, the experimental results illustrate the effectiveness of the presented single-processing method. The analysis results show that the de-noised signal maintains desirable distributed measuring ability and frequency spectrum information. Finally, an existing bridge is continuous monitored by a 20-m-long fiber optic sensor and a long-gauge packaged FBG sensor under moving traffic loads. It is found that the minimum strain resolution is improved to 15 με, while the sampling rate is 0.017 s. The effectiveness of the signal process is verified.
Verification of Joint Input-State Estimation by In Situ Measurements on a Footbridge
An existing joint input-state estimation algorithm is extended for applications in structural dynamics. The estimation of the input and the system states is performed in a minimum-variance unbiased way, based on a limited number of response measurements and a system model. The noise statistics are estimated, as they are essential for the joint input-state estimation and can be used to quantify the uncertainty on the estimated forces and system states. The methodology is illustrated using data from an in situ experiment on a footbridge.
Bayesian Regression Trees as Surrogate Models for the Health Monitoring of Civil Infrastructure
Structural Health Monitoring (SHM) of civil infrastructure often depends on algorithms which detect changes with time in measured data. However, such algorithms can be susceptible to raise false alarms if the structure changes as the result of benign operational or environmental variations. In the case of a bridge, such variations are often caused by temperature effects or traffic loading. One approach to alleviate this problem is to model the effects of the latent (environmental and operational) variables on the measured quantities and subtract them out before change detection algorithms are applied. Recently, it has been shown that Gaussian Process (GP) models are an effective means of developing appropriate surrogate models. However, the GP approach runs into difficulties if changes in the latent variables cause the structure of interest to abruptly switch between regimes. A good example here is given by the Z24 Bridge in Switzerland which completely changed its dynamical behaviour when it cooled below zero degrees Celsius as the asphalt of the deck froze. The solution proposed here is to adopt the recently proposed Treed Gaussian Process (TGP) as an alternative. The approach is illustrated here on data from the Tamar Bridge in the UK which shows marked switching behaviour in certain dynamical characteristics. It is shown that a treed linear model is in fact sufficient to solve the problem.
Proton Radiation Sensitivity of Fiber Bragg Gratings for Particle Colliders
The technology based on Fiber optical Bragg Grating Sensors (FBGS) is very attractive for the structural and environmental monitoring of particle colliders, which require a very precise alignment of their structural components and their sensor elements. Sensors for this purpose need to be unaffected by the extreme radiation cocktail of particles from a collider particle physics experiment and its extremely high magnetic fields. The radiation effects on FBGS with respect to more conventional radiation environments such as space or nuclear installations have been studied for more than a decade and the overall FBGS radiation sensitivity is understood up to a reasonable level. However, significantly fewer studies have been conducted on the sensitivity of FBGSs against proton radiation that is significant in hadronic collider experiments. In this work, the effect of the proton irradiation on the strain and temperature sensitivity were investigated for several FBGS arrays made from different manufactures and with different fiber coatings like Ormocer, acrylic and polyimide. The FBGSs were irradiated with a proton energy of 13.3 MeV in air at room temperature, reaching a total absorbed dose rate of 10 Mrad (100kGy). Based on the positive obtained results, a displacement transducer has been developed for use in the alignment control of the collider sensor elements. The manufactured transducer has a sensitivity of 2,5 pm/micron with a precision of ± 5 microns in a temperature range of -20ºC to 40ºC. and is usable from cryogenic temperatures up to 140ºC.
Vibration Data as a Tool for Continuous Monitoring of Cable Tensile Loads
The in-situ assessment of axial load in cables and prismatic members using flexural vibrations has been investigated for long time. The information about flexural modes in terms of natural frequencies and mode shapes is used to solve an inverse problem for the identification of the axial loads in the presence of a number of assumptions (known material properties) and eventual additional unknowns (i.e. type of constraints at the ends). The available methods for tensile load estimation represent a valuable solution for structural monitoring in operation, but a common drawback is the need for periodic modal identification tests and offline processing of the collected datasets. These techniques are therefore suitable for periodic checks rather than continuous monitoring. However, taking advantage of the most recent developments in the field of operational modal analysis, an automated system for continuous monitoring of axial loads based on dynamic measurements can be developed, as illustrated in this paper through a pilot application. After a review of the methods for axial load identification from vibration properties, the developed monitoring system for continuous monitoring of tensile loads is described.
Vibration Monitoring of Operational Wind Turbine
The modal analysis of a wind turbine has been generally handled with the assumption that this structure can be accurately modeled as linear time-invariant. Such assumption may be misleading for stability analysis, especially, with the current development of very large wind turbines with complex dynamic behavior (nonlinearity, aeroelastic coupling). Therefore in this paper, the inherent periodically time-varying dynamics of wind turbines (and for rotating systems, in general) is taken into account. Based on the Floquet approach, this paper suggests an on-line vibration monitoring algorithm able to detect any trend of a given wind turbine toward an unstable behavior, before any dysfunction or destruction occurs. This turns out to the detection of any change on some residuals using a bank of cumulative sum tests.
An Imaging Method for Impact Damage Localization Using Metal-Core Piezoelectric Fiber Rosettes
As a novel piezoelectric device with small size, Metal-core Piezoelectric Fiber (MPF) has great potential to be used as a structurally integrated transducer for guided-wave structural health monitoring (SHM). Compared with traditional piezoelectric transducers, MPF exhibits unique directivity in Lamb wave sensing which can help to locate the direction of the impact damage without any information about time of flight or speed of propagation. This paper introduces a MPF rosette which contains three MPFs with the angle 120Ëš between adjacent MPFs. To simplify the integration process to structure and improve the performance of the MPF rosette, a method of packaging the MPF rosette is presented. By coupling the piezoelectric effect to the wave strain field, the directivity of the MPF is derived. In order to determine the direction of an incoming wave, the traditional algorithm measures the amplitudes of MPFs and calculates the wave direction by rosette principles. However, the calculated wave direction is very sensitive to the measurement data which has unavoidable noise and dispersive effect in actual application. To improve the accuracy, a new method which combines an imaging method with the rosette approach is proposed. Its accuracy is validated by experiments and the phenomena in experiments are also discussed.
Reconstruction of Impact on Textile Composite Plate Using Piezoelectric Sensors
The presented study focuses on structural health monitoring techniques with the future goal to predict induced damage and to assess the residual strength of impacted structure. Herein, the primary problem of impact force reconstruction is investigated on a square composite plate with nine attached piezoelectric sensors (foil patches) and shunt resistors. The plate is made from carbon textile using RTM technology. The patches are glued on the side with even surface in a symmetrical pattern. The plate is impacted on the same side. The reconstruction is based on deconvolution method and transfer functions. The efficiency of the proposed approach is tested for all impact locations used to calibrate the system, and also on several additional locations. The interpolation of transfer functions is used to increase the accuracy of the reconstruction.
An Experimental Comparison of Modal-Based SHM Techniques Using Strain or Acceleration Measurements
This work presents experimental results for structural health monitoring methods based on modal properties. Two test subjects are considered for the study - a simple composite beam in T-shape and a more complex and realistic system, a composite helicopter main rotor blade (MRB). Different sorts of sensors are used for the purpose of identifying damage, such as piezo strain sensors and accelerometers for the composite T-beam and just accelerometers for the helicopter MRB. Damage is simulated on the test objects by attaching a small mass to different locations. The damage detection methods studied are the coordinate MAC (COMAC), enhanced COMAC (eCOMAC) and scaled COMAC (sCOMAC). The modal properties from the composite T-beam and from the helicopter MRB are obtained using a least squares fit (PolyMAX) and the various techniques mentioned above are implemented and compared.
Acoustic Wave Propagation and Scattering for Fault Detection in Honeycomb Composite Panels
This paper reports on an effort to develop and demonstrate structural health monitoring—specifically, fault detection and localization—algorithms for composite sandwich panels. The test artifact was a small panel consisting of an aluminum honeycomb core sandwiched between two carbon-fiber face sheets, and featuring a controlled impact damage on one side. Data were collected on the damaged side of the panel using a network of piezo-electric sensors mounted on the face sheet. Two (potentially complementary) algorithms were implemented for detecting and localizing the fault from ultrasound data collected before and after damage. The algorithms use variants of the standard least-squares approach for estimating damage location, and are distinguished mainly by their reliance on qualitative versus quantitative physics models of acoustic wave propagation in the panel. Successful detection and localization of a sub-inch fault is demonstrated.
Structural Health Monitoring as an Enabling Technology for Active Compliant Systems
Morphing of selected aircraft parts is a promising component technology to augment aircraft efficiency, and future aircraft designers will likely make increased use of adaptive structures, introducing Active Compliant Systems (ACS) e.g. to entire wing sections. Consequently, options for better control and monitoring of ACS must also be explored. In this paper, we assess the potential for multi-functionality between a winginstalled Structural Health Monitoring (SHM) system and an ACS used to shape-adapt the top part of a non-planar C-wing. We suggest a possible combination of SHM technologies that distinguishes between the desired adaptive response of the morphing system, aeroelastic loads and material fatigue. Over time, a coupled SHM-ACS system may even be capable of learning how to perform flight maneuvers most efficiently, that is, with minimum strain incurred in the material.
Development of SHM System Using Multiple FBG Sensors for Solid Rocket Motor Composite Chamber
A structural health monitoring system using four fiber Bragg grating sensors (FBG sensors) was developed for solid rocket motor composite chambers. The system was designed to measure a large and fast strain change and to measure acoustic emissions (AE) simultaneously. The strain up to 1% and up to 100 kHz was considered. The system possesses two light sources; a newly developed fourband fiber ring laser and a broadband light source. Using the four-band fiber ring laser, the system can measure strain changes and AE simultaneously. Using the broadband light source, the system can measure high-speed strain changes. The strain sensitivities of FBG sensors obtained from the four-band fiber ring laser system agreed well with those obtained from a broadband light source system. A CFRP beam-bending test was conducted to confirm the possibility of simultaneous measurement of both strain and AE signals from a single FBG sensor. A strain gage and a piezoelectric AE sensor were used for reference. In the test, it was confirmed the strain change up to almost 1% was measured from the developed system. AE detection by an FBG sensor and a piezoelectric AE sensor, however, do have slight differences. The developed system will be applied to composite structures in aerospace engineering fields, such as Epsilon Launch Vehicle, which is under development by the Japan Aerospace Exploration Agency (JAXA).
Proof-of-Concept Study of Smart Panel for Space Structures
Verification of a spacecraft structure is a process in which the structural mathematical model, usually finite element model, is compared with the test results to confirm the validity of the structural design. The ultimate structural requirements with the high accuracy and the high stability motivate space engineers to evaluate the structural issues, such as micro-vibration and zero thermal expansion, more precisely on the ground tests. In addition, it would be significant to verify the structural design using the data obtained on orbit since the ground test environments cannot be perfectly equal to the space environments. Author proposed “Sensor Integrated Smart Panel for Space Structureâ€ to enable both the precise structural measurements and the verification on orbit. In this study, fiber optic sensors were integrated with a honeycomb sandwich panel, which consist of CFRP skins and Aluminum honeycomb core. The size of the panel is 300 mm x 300 mm and the weight is approximately 500 grams including mechanical inserts. Four sensor lines with over 30 FBGs were integrated between the CFRP skin and the honeycomb core in the process of bonding. The most remarkable feature of the panel is the design of the optical fiber interface at the edge of the panel. Optical connectors were also integrated with the panel and user (e.g. satellite integrator) can easily access to the FBGs with FC/APC interface without any special tools. Then, the panel was tested in the thermal vacuum chamber to evaluate the durability and the performance of the sensors under the space environment. As a result, the smart panel could endure high vacuum (below 10-5 Pa) and low temperature (below 200K). The sensor output was very stable and low noise, 0.6με in standard deviation within 1°C temperature variation in 30 minutes.
Nonlinear Dynamic Behavior of Impact Damage in a Composite SkinStiffener Structure
One of the key issues in composite structures for aircraft applications is the early identification of damage. Often, service induced damage does not involve visible plastic deformation, but internal matrix related damage, like delaminations. A wide range of technologies, comprising global vibration and local wave propagation methods can be employed for health monitoring purposes. Traditional low frequency modal analysis based methods are linear methods. The effectiveness of these methods is often limited since they rely on a stationary and linear approximation of the system. The nonlinear interaction between a low frequency wave field and a local impact induced skin-stiffener failure is experimentally demonstrated in this paper. The different mechanisms that are responsible for the nonlinearities (opening, closing and contact) of the distorted harmonic waveforms are separated with the help of phase portraits. A basic analytical model is employed to support the observations.
Reconfigurable Architecture Based on Programmable System on Chip and Versatile Interface for Multi Sensors Instrumentations Applied to SHM: Case of an ATR72 Wing Panels
In this paper, multi diagnostic techniques based on guided waves, electromechanical approach and modal analysis are instrumented using a unique programmable SoC (System on Chip). It includes a Cortex M3 32 bits central processing unit (CPU), analog and digital reconfigurable parts based on programmable logic device (PLD). Therefore, damage assessments algorithms are therefore managed by the calculator, the sensor interface is performed through a charge amplifier inside the SoC and the data are transmitted by an Ethernet controller. To cover large areas, the system is fully scalable and accepts a hardware upgrade. The study begins with a complete hardware description. The migration from a test bench composed from a computer, acquisition card and waveform generator to fully integrated solution is also explained. Assessments algorithms based on classical Pitch catch, pulse echo and EMI are detailed. The first prototype is presented and measurements tests performed with specific piezoelectric sensors bonded to a composite specimen extracted from an ATR72 wing panel are exposed. Guided waves are actually used to locate delamination into simple areas situated between the ribs. In the other hand, electromechanical technique is exploited to detect cracks, disbond and delamination beside the fasteners and the ribs.
Structural Health Monitoring-Based Methodologies for Managing Uncertainty in Aircraft Structural Life Assessment
The uncertainty inherent in most aircraft structural maintenance methodologies necessitates inspection intervals, which are established based on either assumed levels of damage or the results of fatigue tests. Maintenance approaches that rely on only scheduled inspections have an intrinsic amount of uncertainty and risk because intervals do not reflect the loading and damage history of each specific aircraft. This risk is more pronounced in composite aircraft, because damage is often not visually apparent. Human factors in labor-intensive inspections make this damage easy to miss when performing tedious inspections of the entire aircraft [2]. This work presents two case studies of structural health monitoring (SHM) methods that are designed to reduce the risk in aircraft maintenance, as well as the cost of frequent, lengthy inspections. The first is an impact identification system which is capable of locating impacts to a full-scale fuselage using only three sensors. This impact identification method is able to quantify the severity of impacts as well, allowing maintenance personnel to focus inspections on areas that have sustained frequent and/or high amplitude impacts. Using this method, over 97% of impacts to a heavy lift helicopter fuselage are located within 9 inches of the true impact location. The second case study details the development of a non-contact wide area inspection method which has the potential to reduce inspection times and uncertainty as compared to labor-intensive inspection methods such as coin tap testing. This inspection method exploits the nonlinear forced vibration characteristics of damaged areas through surface velocity measurements acquired by a scanning laser vibrometer. By comparing the structure’s response to forcing functions of differing magnitudes, the local nonlinear characteristics of damage are identified. This automated inspection method is shown to be effective in locating subsurface damage in composite helicopter panels, and has the potential to reduce both labor costs and damage detection uncertainty.
Assessment of Fibre Optic Sensor Architectures for Structural Health Monitoring
Fibre Optic Sensors (FOS) present several advantages over their conventional electrical counterparts for Structural Health Monitoring (SHM) of aerospace structures. These sensors are dimensionally small and can be readily embedded into composite structures, or bonded to their surface with minimal effect on weight or aerodynamic characteristics. They also do not generate, and are immune, to Electro-Magnetic Interference (EMI), thus they do not affect neighboring electrical systems or avionics. Fiber Optic Sensors have been widely used for load monitoring in discrete or distributed architectures. Additionally, Fibre Bragg Grating (FBG) sensors, a family of FOS, have been explored for the detection of material acoustic waves and damage. Three sensing system configurations, coupled with the use of FBGs, have been studied and reported in the literature for the detection of material acoustic waves; namely, (1) the use of tunable lasers as light source and photodiodes as the light sensors; (2) Broad Band light Sources (BBS) and Arrayed Waveguide Gratings (AWG); and (3) BBS with cascaded, or pairs of phase-shifted FBGs. The first two configurations also offer the ability to perform load monitoring, through strain measurement, in addition to material acoustic wave and damage detection. In an effort to advance the field of Structural Health Monitoring and its implementation in aircraft applications, these three sensing system architectures were evaluated for their capability and ease for the detection of material acoustic waves and damage. Tests were performed using a realistic and aircraft representative skin composite panel, a quasi-isotropic Carbon Fibre Reinforced Polymer (CFRP) skin panel. In the performed evaluation, material acoustic waves generated by a piezoceramic transducer were accurately and reliably detected using the first (1) and last (3) techniques. The first architecture was implemented effortlessly and the last presented, inherently, less complexity. The use of the AWG, in the second (2) architecture, presented comparatively additional challenges to the detection of the generated acoustic waves due to the requirement for a high power light source. As a result of this evaluation, the first (1) technique was subsequently selected and assessed in damage detection trials with promising results. Due to the demonstrated success of these trials, the experimental composite skin panel, along with the demonstrated damage detection technique, are being integrated into the SHM infrastructure being developed at the National Research Council Canada (NRC). This infrastructure consists of structural platforms that range in complexity from a simple 2 m long aluminium beam to full scale aircraft structures – a CF188 wing and a Bell 206 helicopter tail boom. Additionally, such infrastructure offers full scale evaluations and testing of damage detection techniques and technologies and SHM capabilities employing realistic loads and spectra optionally in varying environment conditions.
Simulation Studies of Spectral Subtraction Based Temperature Compensation of FBG Sensor for Structural Health Monitoring
Fiber Bragg gratings (FBGs) are optical fiber based sensor that are widely used for structural health monitoring. However, the strain and temperature components of the measurement are coupled. This paper discusses a method to decouple the two parameters using spectral subtraction.
Research on Spatial Filter Based Structural Imaging Method and the Evaluation Research on Complex Composite Structures
A linear sensors array based damage imaging method called spatial filter are studied by some researches. It is a kind of directional filtering method that can be used to extract information of damage from a particular direction of signals and the other unwanted information from other directions is filtered out. But this method is only applied to damage monitoring of aluminum structures at current stage. In this paper, a spatial filter based impact and damage imaging method is proposed, which is implemented by adopting complex Shannon wavelet transform to construct analytic signals of time domain signals of piezoelectric sensors (PZTs) array to participate in signal synthesis process of spatial filtering imaging on composite structures instead of using structural parameters to fulfill the process on aluminum structures. An integrated multi-channel PZTs array scanning system (ISS) is developed for the purpose of impact and damage monitoring. An experiment is performed on a carbon fiber composite oil tank of an aircraft. The results show the promising performance of the system and that the damage position and impact angle which are relative to PZTs array can be estimated efficiently by this method.
Application of the Triple Correlation Technique to Damage Detection of Structures
This paper reports the Triple Correlation Coefficient technique for impact damage detection in composite structures. Low-frequency modal vibration is used to excite a composite plate. A surface-bonded piezoceramic stack actuator is applied for excitation. Non-contact laser vibrometry is used for response measurements. The method correlates the fundamental frequency of signal response with higher harmonics appearing in the signal as a result of damage.
Fiber-Optic-Based Quality Control of Aerospace CFRP Structures
This study proposes an “advanced smart quality assuranceâ€ concept to monitor the curing process of carbon fiber reinforced plastic (CFRP) and assure its cured quality. The authors embedded fiber Bragg grating (FBG) optical sensors in the inter-laminar toughening layer of aerospace-grade CFRP laminates to monitor the local residual strain depending on applied cure pressure. Inter-laminar fracture toughness was then evaluated from double cantilever beam (DCB) tests. A good correlation was obtained between the residual strain value and the fracture toughness, demonstrating that the proposed CFRP quality assurance method is effective to manufacture highly-reliable aerospace CFRP structures.
Aerospace Industry Steering Committee on Structural Health Monitoring (AISC-SHM): Efforts to Standardize and Streamline SHM Utilization Sensors
As a result of the increased attention that SHM technologies are receiving from industry, a new group was launched to promote industry-wide cooperation on the use and development of SHM systems. The Aerospace Industry Steering Committee on Structural Health Monitoring (AISC-SHM) was formed six years ago by an international team of industry and research representatives. It currently operates under the SAE International Aerospace Division in technical committee G- 11 SHM. The AISC-SHM comprises members from the world’s major civil and military aircraft manufacturers and suppliers, aircraft operators, regulatory agencies, government agencies, SHM technology providers and researchers. Its primary mission is to provide approaches for standardizing integration and certification requirements for SHM which includes system maturation, usage, maintenance, supportability and expansion. This paper presents the contents and the philosophy behind the first AISC-SHM document which is intended to address these needs. The ‘Guidelines for Implementation of Structural Health Monitoring on Fixed Wing Aircraft’ document, which will be published as an SAE Aerospace Recommended Practice, was produced by the AISC-SHM using inputs from the technical committee. This SHM guidebook will provide information and guidance that will support the adoption of SHM practices and help ensure the regulatory guidance needed for the safe incorporation of SHM in aerospace structures.
Validation and Verification Processes to Certify SHM Solutions for Commercial Aircraft Applications
Reliable Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition and signal the need for human intervention. While ad-hoc efforts to introduce SHM into routine aircraft maintenance practices are valuable in leading the way for more widespread SHM use, there is a significant need for formal SHM technology validation and certification processes to uniformly and comprehensively support the evolution and adoption of SHM practices. Such a plan must contain input from aircraft manufacturers, regulators, operators and research organizations so that the full spectrum of issues including design, deployment, performance and certification is appropriately considered. Currently, there are no guidelines for SHM system designers or agreed-upon procedures for assessing the performance of SHM systems. Through the operation of the FAA’s Airworthiness Assurance Nondestructive Inspection Validation Center (AANC), the FAA secured its mission to provide independent validation of technologies intended to enhance the structural inspection of aircraft. A natural extension of this mission is to incorporate SHM in these validation efforts. The AANC’s validation approach is designed to address the equipment, the inspection task, the resolution required, the inspection procedures, the conditions under which the inspection will occur, and the potential inspector population. To carry out the validation process, knowledge of aircraft maintenance practices must be coupled with an unbiased, independent evaluation. The AANC has designed, implemented, and analyzed the results from a wide range of statistically-relevant experimental programs to quantify the reliability of inspection methods as deployed at commercial aircraft maintenance facilities. Much of this methodology to quantify NDI performance can be adapted to the validation of SHM systems. However, it is important to recognize the unique validation and verification tasks that arise from distinct differences between SHM and NDI deployment and flaw detection. An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system.
Large Scale Monitoring of CFRP Structures by Acousto-Ultrasonics—A Flight Test Experience
This paper outlines an Acousto-Ultrasonic Structural Health Monitoring system for flight test purpose, dedicated to damage assessment in large scale areas of structures made of Carbon Fiber Reinforced Plastics. In the second part an overview on a long-term flight test is given and results with special regard to the operational and environmental conditions are presented.
Reliability of Co-Bonded Piezoelectric Sensors on CFRP Structures
In order to replace conventional Non Destructive Testing (NDT), SHM needs to be at least as reliable and cost effective as the competing NDTTechnology. The reliability of the permanently installed sensor is among others pending on the link between sensors and structure. In the past years an alternative to the current secondary bonding process has been investigated, the co-bonding of sensors. The co-bonding shall reduce the recurring costs for sensor installation compared to secondary bonding while keeping the reliability on the same level. Co-bonding is leading to several benefits regarding the manufacturing process, as the sensors are applied outside the critical production path, but is leading at the same time to challenges regarding sensor and process requirements. In past work, it has been demonstrated that piezoelectric sensors do have the capability for co-bonding. In the present work, adjunctive issues are outlined. This comprises to prove that the sensors do not derate the mechanical properties of the adherent CFRP material and to verify the durability of the sensors. Both, proof of non-disturbance and sensor durability, are demonstrated by mechanical tests on coupon level. Over 400 coupons are tested at different locations within Europe in the frame of the EU funded project SARISTU, of which this paper is presenting a selection of first results. Other important tasks, as the sensor network connection and protection are touched and an outlook is given.
Method for Real-Time Health Monitoring of Large Polymer Composite Structures Using Carbon Nanotube Networks
In this paper, a novel and practical method has been developed for real-time detecting, locating and quantifying damages that occur at one or several locations in large polymer composites structures (LPCSs) using carbon nanotube networks. In this real-time structural health monitoring (SHM) method, multiwalled carbon nanotubes (MWCNTs) were embedded into the epoxy resin to form networks of multifunctional in-situ sensors. This modified epoxy resin was then infiltrated with long kevlar fibers to make kevlar fibers/epoxy/MWCNTs composite plates. A grid of points made of electrically conductive silver paint was mounted on the surface of the plate. The electrical resistances between the grid points were measured and used as a reference set. Drilled holes and impact testing were performed to simulate damages. The experimental results show that the significant changes in electrical resistance across the grid points correspond exactly to the damages that occur in the large plate. As such, this new method can provide strong potential to detect, locate and quantify damages in LPCSs made of electrically non-conductive fibers and CNT networks.
Application of Incremental Gaussian Mixture Models for Characterization of Wind Field Data
Structural responses and power output of a wind turbine are strongly affected by the wind field acting on the wind turbine. Knowledge about the wind field and its variations is essential not only for designing, but also for cost-efficiently managing wind turbines. Wind field monitoring collects and stores wind field time series data. Over time the amount of data can be overwhelming. Furthermore, the correlation among the wind field statistical features is difficult to capture. Here, we explore the use of online machine learning to study the characteristics of wind fields, while effectively condensing the amount of monitoring data. In particular, incremental Gaussian mixture models (IGMM) are constructed to represent the joint probability density functions for wind field features, whose parameters are continuously updated as new data set is collected. The monitoring data recorded from an operating wind turbine in Germany is employed to test and compare the IGMM with conventional machine learning approach that uses an entire historical data set.
Wireless Active Sensing Platform for Structural Infrastructure Monitoring
Embedded sensing for structural health monitoring (SHM) is a rapidly expanding field, propelled by algorithmic advances in SHM and the ever-shrinking size and cost of electronic hardware necessary for its implementation. Although commercial systems are available to perform the relevant tasks, they are usually bulky and/or expensive because of their high degree of general utility to a wider range of applications. As a result, multiple separate devices may be required in order to obtain the same results that could be obtained with an SHM-specific device. This work presents the development and deployment of a versatile, wireless active sensing platform (WASP), designed for the particular needs of embedded sensing for multiscale SHM. The WASP combines the conventional data acquisition ability to record voltage output (e.g. from strain or acceleration transducers) with ultrasonic guided wave-based active sensing, and a seamlessly integrated impedance measurement mode, enabling impedance-based SHM and piezoelectric sensor diagnostics to reduce the potential for false positives in damage identification. The unique combination of sensing modalities makes the WASP an excellent new platform for embedded SHM. The motivation, capabilities, and hardware design for the WASP are reviewed, and deployment examples are presented, each demonstrating an important aspect of embedded sensing for SHM.
Unmanned Robot System for Structure Health Monitoring and Non-Destructive Building Inspection, Current Technologies Overview and Future Improvements
Inspection is a major issue with respect to the integrity and health of engineering structures. Many of those structures deteriorate at locations that are difficult to assess (for example a roof of a very tall building). State-of-the-art inspection methods are mostly visual inspections completed by trained inspectors, where inspections are time consuming, costly and dangerous. In order to reduce the risk and cost and increase the accuracy of inspection/monitoring, unmanned robotic systems (aerial or ground based) can therefore be of invaluable help. The research groups at both Fraunhofer IZFP and Saarland University have carried out experiments using micro aerial vehicles equipped with an off the shelf camera for building inspection and structural health monitoring over years. Many of the state-of-the-art technologies regarding image processing, robotics and 3D model reconstruction have been tested and evaluated with real engineering applications. The first part of this paper will be discussing our experience, the limitation of state-of-the-art technologies and experimental results of building inspection/monitoring, which includes methods of displaying image results in both 2D and 3D. Based on the results of the first part, the second part of the paper will focus on the improvement of image acquisition and stabilization. The last part of the paper will be discussing how other developments in robotics system (both aerial and ground) have a potential to fulfill future requirements in terms of building infrastructure life cycle management.
Wireless Chipless Passive Electromagnetic Transducers for SHM Applications
The energy autonomy of wireless sensors is crucial especially for buried or inaccessible sensors that are often the case in SHM applications. This problem can be overcome by using classical passive sensors (RFID or SAW sensors) but they suffer from a problem of low reading distances (<10m). For few years emerged a new kind of passive sensors using electromagnetic transducers, combining very high autonomy, high resistance to harsh environment, potential interrogation distance higher than SAW sensors and wide variety of sensing principles. We started to work in this new research field in 2005 and several sensor principles have been validated in our lab (pressure, temperature, stress) with FMCW (Frequency Modulated Continuous Wave) Radar interrogation.
Structural Health Monitoring During Assembly of Aerospace Structures
With the objective of verifying and validating the novel methodology for Structural Health Monitoring before, during and after an assembly of aerospace structures, by use of multiple PAMELA SHM™ system wirelessly controlled [1][2], a SHM test campaign on a simplified six component aluminum wingbox was performed. The methodology was implemented and a full assessment of its advantages together with identification of potential unforeseen problems was made. During the tests was found out that each one of the different assembly phases, by mechanical fastening of all interface screws, induces complex changes into ultrasonic responses of each one of the joining components, in a form of new and different ultrasonic wave trains than the initial ones with the components separated. The direct consequence of these effects, which emerged and is of utmost importance for efficient and reliable SHM in the near future, is the need to first completely understand all changes and different boundary condition effects on ultrasonic wave structural responses, not just during the assembly but also for any other structure life cycle phase. The ultrasonic tests in this first test campaign phase were performed by monitoring only one structural component, the upper skin, in the pulse-echo inspection mode, for several test configurations of interest and in sixteen different boundary conditions of the subassembly, starting with the skin alone until the bay wingbox was completely closed. Tests were performed by use of one integrated Phased Array transducer (PhA III) [3][4], surface bonded onto the interior face of the upper skin, by one integrated on-board PAMELA III electronic device [1] electromechanically installed above the transducer and controlled wirelessly by PAMELA SHM™ System Controller software on a external host computer. A brief overview of typical changes in ultrasonic time signals for several assembly steps is presented in the paper, with the recommendations for future actions, aimed to make easier interpretation and identification of these structural changes.
A Wireless Data Acquisition System for Acoustic Emission Monitoring
As SHM researchers have gone about trying to determine the best methods of damage prognosis in civil, mechanical, and aerospace structures, acoustic emission (AE) testing has emerged as a technique that can provide an estimate of the location, severity, and type of damage in a structure. AE methodologies use stress waves generated by the mechanical deformation of a monitored material to detect the presence and severity of damage locations. AE techniques, which involve the analysis of frequency content between 1 kHz and 1 MHz, have proven effective in detecting the onset of cracking and other early-stage failure in active structures such as airplanes in flight, but they suffer from the need for large numbers of sensors to be deployed on a monitored structure. Outside of the laboratory, AE methods have additional drawbacks that have hampered their adoption in real-world SHM applications. Primarily, these approaches require traditionally bulky and expensive equipment, as well as a data acquisition system that can sample and store sensor data at very high sampling rates (up to 25MHz). As a result, per-sensor costs and unrealistic data acquisition requirements have hampered the market adoption of this approach to SHM. In this paper, a new low-cost wireless data acquisition system is presented that is capable of collecting, storing, and wirelessly transmitting AE data at rates of up to 12.5MHz. This battery-powered device has a very small form factor, includes a four-bit software-selectable gain stage, and has been designed with the ability to process AE data as it is collected, improving its battery life and allowing its wireless connection to only be used when one of a set of trigger events have been detected.
On Development of a Fully Operative Piezoelectric-Based Vibration Energy Harvesting System
A new generation of sensor and actuator, e.g. for SHM application, is forcing us to look for new energy sources which would be used instead of battery & classical power generators. Future of (aerospace) industry goes toward sensors and/or actuators embedded, permanently bonded or even inter-layered into main structures. Supply of these items with the needed power is still an opened issue. Classical power supply (battery and generators) have several drawbacks like the short life of batteries with related maintenance and substitution, making impossible the embedding and interlayering of new concepts sensors/actuators due to the lack of accessibility. Generators have similar drawbacks with the additional problem of power transferring and consequent complexity and weight penalization. Obtain energy from no conventional source (Energy Harvesting - EH) will become more and more important over the years. In airplanes (like in bridges, building, land vehicles …) a constant and unused source is the mechanical energy lost in vibration that can be converted in exploitable. Harvest energy from structural vibrations offers several advantages. Firstly, the proposed EH system have extremely low weight and small dimensions, as a result energy can be generated in situ, without need for power transfer from central generator to peripheral sensor/actuators, minimizing thus the cabling. Secondly the system can be mounted everywhere, is extremely flexible, highly configurable. The only requirement is a vibration level high enough to activate the energy harvesting. Thirdly, the system is piezoelectric-based, highly compatible with a large family of (aeronautics) sensors/actuators that share the same technology. Potentially the proposed energy harvesting system can be integrated directly in sensors/actuators system, maybe sharing the same piezoelectric elements that can recollect the energy that they itself needs. Finally the system is maintenance-free; it can be surface mounted, embedded or inter-layered exactly in the same way as the powering sensor/actuator. Other potential advantages will be the additional damping provided to vibrating structure. In test campaign newest piezoelectric patch was used as voltage generator due to its flexibility and low thickness. An electronic device based on commercial integrated circuits is used to rectify voltage and store energy in condensers for future utilizations. Many parameters must be considered during optimization, here the most important ones will be discussed. In order to evaluate
In Field Application of Rapid Roadway Inspection System Using Vehicle-Mounted Multi-Modal Sensing
U.S. roads are in critical need of repair. This is evident through personal driving experiences and professional organizations like the American Society of Civil Engineers, who performed a nationwide survey in 2013 that yielded a report card rating of D, which is nearly failing. Going hand in hand with this critical need for repairs, is a critical need for pavement condition surveys. These city-wide surveys provide a condition label for every street based on type, severity, and density of distresses. Since cities lack the resources to fix all roads at once, they must rely on these surveys to prioritize and develop cost effective maintenance and repair plans. Conventional pavement condition surveys require a rigorous in-field inspection by experts, which is time consuming, unsafe for inspectors, and causes traffic interruptions. Alternative vehicle-mounted sensing approaches exist, but are not economical due to expensive sensing technology and costs for experts to drive the vehicle and manually process data. The VOTERS project (Versatile Onboard Traffic Embedded Roaming Sensors) aims to develop a mobile pavement inspection system that is more affordable and can be repeated more often. This goal will be realized through the development of a compact, hidden, and fully automatic system that can be installed on service vehicles such as Post Office, UPS, FedEx, or public transportation. In order to handle the complex nature of pavement condition and the challenges of mobile sensing, the system will be comprised of an innovative and affordable multi-modal sensing array that uses data fusion concepts. Currently, a test vehicle has been developed that is outfitted with an acoustic sensor, dynamic tire pressure sensor, camera, mm-wave radar, and laser height sensor. This paper will introduce this system and discuss its recent application and results in the City of Brockton Massachusetts. A data fusion machine learning approach for mobile pavement condition assessment will be developed and shown to have accurate and repeatable results when implemented in the field.
Applied Element Method Framework for Vibration-Based Condition Assessment
The ultimate objective of structural health monitoring (SHM) is to track the progression of natural deterioration and event-driven damage in civil infrastructure to inform maintenance and prevent unforeseen failure. To be meaningful to engineers and maintenance officials, the information from the sensor network must be distilled into actionable measures, preferably in meaningful engineering quantities. This ability to physically quantify structural performance through SHM is particularly attractive within post-hazard environments, as it could be leveraged to inform first responders, inspectors, and owners of potential vulnerabilities within a structure. Although many techniques have been introduced in recent years to detect the presence and location of damage through vibration-based monitoring, a methodical approach for estimating the extent of damage in meaningful engineering quantities remains elusive and largely unproven on full-scale, real-world structures. Furthermore, case studies have been largely limited to traditional causes of damage such as, natural disasters and normal deterioration. This study investigates the use of vibration-based structural identification and damage detection techniques applied to structural evaluation of conventional building components subjected to survivable blast loading. Experimental testing was performed in a full-scale industrial building where a partially-grouted CMU infill wall, precast prestressed double-tee roof, and an interior steel frame were studied in depth. Modal parameter estimates of the infill wall, which is the focus of this paper, as well as the roof and steel frame, were obtained through experimental modal analysis both prior to and after blast testing to establish both baseline and post-damage datasets. As an alternative to finite element model updating, structural identification of the building components tested is currently being pursued through parameter tuning of an applied element model. The Applied Element Method (AEM) has proven to be particularly attractive for analysis of structures under extreme loads and offers several advantages over the finite element method (FEM) for
The Importance in Load Positioning During Experimental Load Testing of Highway Bridges
Experimental load rating and testing of highway bridges is the most common approach for determining the load capacity a bridge can withstand. In many respects, it is a method of determining the suitability of a bridge to be in-service. Strain transducers are positioned at various critical locations of the structure to capture parameters such as neutral axis of bending, transverse load distribution factors, impact factors and end fixity. The bridge is loaded at predetermined locations using a calibrated test vehicle. One such problem that can exist however, particularly in heavily deteriorated bridges, is the load may not adequately shed to all structural members. As a result, the strain measurements may be fairly low and be significantly affected by any noise within the sensor system. This paper discusses the field testing of a 3 span simply supported bridge superstructure at the end of its service life. Substantial deterioration from corrosion of the steel girders and heavy spalling of the bridge deck were noted at the time of testing. A load test was completed with a custom developed wireless sensing system monitoring strain at the midspan of each girder. Neutral axis of bending, transverse load distribution factors and impact factor response were each determined using methods outlined in department of transportation protocols. Upon processing of the data it was noted that the system produced +/- 0.1 microstrain of peak-to-peak noise. Select load cases illustrated the inability of some load to adequately shed to an exterior girder. This, in effect, reduced the signal strength of the actual strain measurement by highlighting the measurement noise. A sensitivity analysis is discussed to show what effect +/- 0.1 microstrain has in determining the accuracy of the neutral axis, distribution factor and load rating of the bridge. This paper demonstrates the importance of vehicle positioning when determining the appropriate load testing values used in load rating.
Quantifying Bridge Deck Corrosion with Ground Penetrating Radar
Throughout the life cycle of a bridge deck the most expensive part to maintain is the deck. The deck is replaced at least once and rehabilitated many times. However, the deck is the most important part of the bridge because it carries the traffic yet it is also exposed to harsh elements like deicing salts during the winter months. These deicing salts penetrate through the concrete and cause the reinforcing steel to corrode. The results of corrosion are cracks, delaminations and spalls. Current inspection methods, like visual inspection and chain drag, used to determine the deterioration condition of reinforced concrete bridge decks do not provide enough quantitative information about the deck. Nondestructive evaluation techniques like ground penetrating radar (GPR) have the ability to accurately and efficiently assess the internal composition of the deck by rapidly scanning over the surface at highway speeds. Previous research has shown that the electromagnetic signals emitted from GPR antennas are attenuated, or their amplitude is reduced when corrosion of the reinforcing steel is present. However, it is not completely clear how the various components of corrosion (i.e. cracking, rust or chloride) contribute to the signal attenuation at the rebar level. This research seeks to break down each component of corrosion individually and by using computational modeling, investigate the signal attenuation at the rebar level. In addition to previous literature, pieces of a highly corroded bridge deck once in-service were used to understand the physical effects of rebar corrosion.
Pavement Assessment Using a Dynamic Pressure Sensor System
Various sensor systems are wildly used in inspecting the nation’s civil infrastructure of roads and bridges. Unfortunately, infrastructure integrity cannot be conveniently evaluated in an economically or a time efficient way using these existing systems. This work presents a non-destructive and non-contact acoustic sensing approach for fast inspection of roads and bridge deck conditions with vehicles running at normal speed without stopping traffic. This approach, which uses an instantaneous / realtime dynamic tire pressure sensor (DTPS), possesses the capability to inspect pavement conditions from a moving vehicle. By measuring the dynamic pressure changes (the pressure variation rather than the static pressure) inside the tire, useful signals from tire/road interaction can be amplified and isolated from environmental noise. Comparisons between the DTPS, a ground-mounted accelerometer, and a directional microphone were made successfully. In addition, the DTPS can also measure the dynamic response of the tire-road interaction and reduce the cost of currently used road profile measuring systems which use vehicle body-mounted profilers and/or axle-mounted accelerometers. A prototype real-time DTPS with both wired and wireless communication systems has been developed together with a separately designed rotating energy harvester. Both have been demonstrated on a testing van at speeds from 5 to 80 mph. A data analysis algorithm has been developed and optimized to enhance the detection accuracy and to remove axle dynamic motions from the measured DTPS data and to find the transfer function between the dynamic tire pressure change and the road profile. Comparisons between the DTPS response and known Pavement Condition Index (PCI) values for different roads have been carried out. The road profile resolution is approximately 5 to 10 cm in width and sensitivity is 0.3 cm for the height road surface features at driving speeds of 5 to 80 mph.
Verification of the Slope Stability of Flood Prevention Dams with Fiber Optic Sensors
The stability of flood prevention dams is critical for a reliable protection from catastrophic events. Graz University of Technology carried out a series of life-size experiments to determine the slope stability of flood prevention dams in the vicinity of a water power plant. A measurement program based on conventional geodetic techniques and fiber optic sensor was developed for the high frequent monitoring of the dam deformations. The measurement concept, the calibration of the fiber optic sensors and the comparison between the different measurement techniques are presented in this paper. We demonstrate that a precision of better than 7μm can be achieved with the fiber optic measurements based on FBG sensors. The high resolution fiber optic sensors complement the geodetic deformation measurements and enable detailed investigations on small-scale deformations caused by the creeping behavior of the dam.
Validation of the Rotation Algorithm for Earthquake Damage Estimation
A rotation algorithm was previously developed by the authors to estimate residual displacements in single columns subjected to earthquake loading. The objective of this paper is to determine the optimal number of sensors and their placement. For that purpose, numerical models for single columns deflecting in single and double curvature were developed where the nonlinear behavior was verified using laboratory test data. The algorithm is applied to the numerical simulations and its capability to capture single and double curvature deformations is verified. The accuracy of the algorithm is tested and it is found that the predicted displacements are within 2% of the true displacements with 50% confidence and within 4% of the true displacements with 90% confidence. Guidelines are provided for the optimal number of sensors, their placement and the functional form to be used for the estimation of the displacements. In particular, it is found that four sensors are sufficient for this level of accuracy. In addition, at least one sensor should be placed in the vicinity of expected plastic hinge locations, such as the base of all columns and near the top for double curvature columns. It is also shown that a fourth order polynomial as an estimate for displacements results in the aforementioned level of accuracy. The presented algorithm serves as a simple, accurate and reliable option for rapid damage assessment after a major event due to its ability to be embedded in the microprocessors of the sensors and the low cost of the sensors involved.
RAPID System for Crack Detection in Riser Pipes
Risers are the major component of offshore oil platforms, and any failure can have serious environmental and economic consequences. But monitoring the structural integrity of riser pipes in service is challenging. Utilizing SMART Layer® technology, a Real-time Active Pipeline Integrity Detection (RAPID) system has been developed for in-situ structural integrity assessment of a pipeline. The RAPID system consists of a sensor network permanently mounted on the host pipeline, portable electronic hardware and diagnostic software. Previous studies have demonstrated the use of the RAPID system to detect corrosion in pipelines. In this paper, the capability to detect and size cracks is presented. With the support of Shell Global Solutions (US) Inc., a series of tests have been conducted on 4 flat coupons and 2 riser pipes to verify the crack detection capability. It was shown that the RAPID system is extremely sensitive to crack growth, and that a multitude of information can be collected about physical changes on the structure, even in the presence of existing defects. Crack initiation, crack location and crack profiling was demonstrated using minimal calibration information.
Implementation of Digital Image Correlation for Structural Health Monitoring of Bridges
As the civil infrastructure (e.g. bridges) of the global highway system ages, there exists a critical need for structural health monitoring over large areas that is robust, inexpensive and easily interpreted. According to the American Society of Civil Engineers, in 2008, 161,892 bridges were structurally deficient or obsolete. In the next fifteen years, nearly half of America’s bridges will exceed the typical 50- year design life. Thorough inspection of these aging bridges is important to ensure public safety. Currently, bridge health is assessed primarily using qualitative visual inspection, which is subject to variability due to an inspector’s interpretation. Instruments such as strain gages, accelerometers, fiber optic sensors, displacement transducers, etc. are becoming more common for monitoring bridge health. These sensors require external power, cabling/antenna for data transmission, high data acquisition channel counts and only measure at a discrete point or along a line (making it difficult to detect damage outside the sensor’s proximity). To rectify these drawbacks, this paper presents research results of using three-dimensional (3D) digital image correlation (DIC) as a new approach for quantitative bridge structural health monitoring. 3D DIC is a non-contact, full field, optical measuring technique that uses two digital cameras to measure surface geometry, displacement, and strain. Long term monitoring with DIC can be accomplished by imaging the bridge periodically and computing strain, displacement and surface geometry from images recorded at different dates. In this paper, DIC is shown to 1) quantify spalling by comparing subsequent surface geometry measurements, 2) monitor crack width using extensometers established between photogrammetric targets and 3) successfully locate non-visible cracks using full-field strain and displacement measurements. These techniques are first confirmed in laboratory tests. Next field measurements are made on three operational full-scale bridges. This paper also discusses the challenges and solutions to effectively implementing DIC
Dynamic Strain Measurements Using Digital Image Correlation
Many structures in North America are reaching the end of their design lives but some are still fit for purpose while others require repair or replacement. Currently, to assess these structures, subjective visual inspections are relied upon. An alternative technology that can be used is Digital Image Correlation (DIC). DIC is a technique that uses digital images of an object (e.g. a critical element of a structure) before and after load is applied to create a 2-D displacement field for the object. If this displacement field can be measured with a sufficiently high resolution, these displacements can also be used to determine a 2-D strain field. Recent progress in this area has led to the development of contactless strain sensors that are suitable for lab use under quasi-static conditions. The current research explores the possibility of using high speed cameras to measure displacements and strains under dynamic conditions. An initial calibration exercise was performed to determine the potential accuracy of the high speed camera system. Image texture and quality were found to be crucial as measurement errors increased with decreasing texture and image quality. An experiment was then conducted using an axially loaded plate specimen with different amounts of texture and using two different types of lenses. Texture and image quality had a significant impact on dynamic strain measurement accuracy, however, when these factors are optimized, near strain gage accuracy can be achieved.
Reporting the Results of Two Experimental Campaigns on a Pedestrian Timber
The authors carried out two experimental campaigns on a pedestrian bridge, of length 83 m, built by assembling two parallel timber beams and several steel elements. A wireless telecommunication architecture designed by the authors was used to cover the whole length. The report is focused on the comparison of the collected results.
Health Monitoring of a Cable-Stayed Bridge from Traffic-Induced Vibrations
The dynamic characteristics of the Kao-Ping-Hsi cable-stayed bridge, located in between Kaohsiung and Pingtung county, Taiwan, is monitored under daily traffic flow for assessing its health conditions. The bridge is an asymmetric single tower cable-stayed bridge with the main span of 330 m, and side span 180 m. The height of the inverse Y-shaped pylon is 183.5 m, measured from the foundation to the top of pylon. Experimental data were measured from servo-velocity sensors, with six channels, were placed at various locations on the bridge. Data sampling rate was 100 Hz, and system-identification techniques, such as the random decrement (RD) technique and Ibrahim time-domain (ITD) method, were adopted for data reduction. The vibrational modes of the bridge were identified for their natural frequencies and damping ratios under different traffic loading conditions. The loading conditions are classified by the root-mean-square (RMS) deck velocities. The magnitude of the torsion mode of the Kao-Ping-Hsi cable-stayed bridge is found to be one order-ofmagnitude less than that of the transverse mode, and two orders-of-magnitude less than that of the vertical modes. Conventionally, through monitoring the natural frequencies, vibrations induced by traffic flow can be used to reliably monitor the health of the bridge. However, we found that the damping ratios may serve as a more sensitive indicator to describe the condition of the bridge, due to its high sensitivity to the RMS velocities. It is expected that damping ratio may be more sensitive to local change in material properties or component connectivities. Therefore, use of both of the natural frequencies and damping ratios may provide a more accurate description of the health of cable-stayed bridges.
Applications of New Sensing Technologies for Reinforced Concrete Related to Nuclear Facilities: Considerations and Potentials
Nondestructive evaluation (NDE) has been used to investigate construction and aging effects of concrete containment buildings for the nuclear industry. Specific mechanisms causing reduced serviceability may include chemical attacks, physical attacks, and degradation factors such as leaching, alkali-aggregate reactions, freezethaw, fatigue/vibration, corrosion, elevated temperature, and so forth. These degradation mechanisms are consistent with reinforced concrete structures that are non-nuclear related. However, nuclear structures, whether safety related or non-safety related, often have unique structural characteristics (such as steel-lined concrete) which increase the proclivity towards degradation and inhibit inspection. Often, traditional NDE techniques do not offer adequate investigation through the steel liner, because they require access to large surface areas of the concrete to offer meaningful results. Modern embedded sensing technologies may provide opportunities for the indepth evaluation of nuclear, reinforced concrete structures. This paper offers a review of emerging embedded sensor techniques, the benefit of numerical modeling for sensor placement, and critically evaluates sensor applicability to the nuclear industry.
Method of Structural Monitoring for Systems with an Imperfect Sensor Network
There are important objects behind which constant supervision (monitoring) is necessary. According to regulations, on such objects the structured systems of monitoring and management of engineering systems of buildings and constructions are subject to obligatory installation. They provide supervision over the basic systems of object, using a network of geodetic and geotechnical sensors. This network, being the basic source of data about an object state, itself has defects. Nowadays not monitoring of limiting values, but revealing of tendencies becomes the purpose of the stress-strain analysis. With highest resolution and highest recording rate of current instruments the small deformations, caused by the daily temperature changes, can observed. These disturbances are present in the readings of all sensors and, therefore, may appear in the cross-correlation relations. If correlation dependences of a sensor or group of sensors with the majority of others is weakened it testifies to abnormal behavior either sensor, or object. Propose the method of supervision, which map changes in object-network state (behavior) into subspace of correlation characteristics of groups (associations) of sensors with strong correlations. The method provides supervision of changes in object-network state, it is insensitive to the spread in biases and gains of sensors, up most of defects in the network, at the same time, it is identifies and locates drifting and freezing sensors. Presents the results using the method for long-term monitoring constructions covered sports arena.
A Complementary Approach to Condition Assessment of Highway Bridges Using a Quantitative Index Measure
This paper discusses the preliminary stages for the development of a quantitative based performance index measure that will allow inspection engineers to complement existing qualitative inspection protocols. This proposed method seeks to implement select experimental load testing and rating parameters that are commonly computed by department of transportation (DOT) agencies on a selected bridge basis. The performance index measurement was developed to be a function of two particular variables, the neutral axis of bending and transverse load distribution factor. These parameters were selected as they are both independent of the vehicle weight used for exciting the bridge. This is significant in that it would not require inspection engineers to use the same vehicle loading each time for inspection. The performance index serves in a similar manor to that of the condition rating used today only the data will be gathered from strain sensors instead of being based solely on visual observations. The initial stages of the performance index are developed by calibrating the ratio of neutral axis to distribution factor as well as the load rating for the various steel girders of the 3- span stringer/multi-girder bridge tested. This tool would serve to complement existing inspection protocols as an additional index based on quantitative data as opposed to visual inspection findings. While still in the early stages of development, this assessment tool looks to help close a piece of the gap in the need for improved condition assessment methods.
Vibration Monitoring and Live Load Tests of Civil Infrastructure with Interferometric Radar
With advances in technology, interferometric radar is expanding the possibilities for monitoring vibrations and performing live load tests of bridges and other structures. This paper presents an instrument (IBIS-S) that utilizes interferometric radar to measure displacement as a function of time. Displacement of most structural elements in the line of sight can be measured to a precision of 0.01 mm at a sampling rate of 200 Hz in a noncontact manner. Traditional instruments used to monitor vibrations often require contact with the members, or noncontact measurements can only be done over a limited range. Further, traditional instruments can be time consuming to set up and costly to maintain. With interferometric radar technology, data typically are obtained in hours, and the monitoring is done from a remote point, precluding the need to access the structure or disrupt the use. It will be shown that the IBIS-S instrument can be deployed quickly, without interrupting the use of the structure, allowing the user to begin collecting vibration data under live loads within minutes. Included in this paper is a literature review of interferometric radar use on buildings, bridges, and wind turbines. Uses include fundamental frequency and mode shape determination of buildings, bridges, and other structures, as well as live load tests. Interferometric radar can be used successfully as part of a structural health monitoring program, wherein baseline vibration and displacement data are obtained and then the measurement process is repeated periodically. Depending on the structure type, changes in fundamental frequency or mode shape can reveal a loss of stiffness, which may be an indication of critical deterioration. Also discussed are the results of vibration monitoring for a cable-stayed pedestrian bridge. It will be shown that interferometric radar technology can be used to determine the global natural frequency of a cable-stayed bridge and the local natural frequency of individual elements (towers, cables, railing, etc.). Limitations of the technology are also discussed.
SHM of a Masonry Chimney after a Lightning Accident
The paper focuses on the SHM works carried out on a historical masonry chimney located in a former industrial complex in Guimarães city centre, Portugal. The chimney was hit by a lightning in the morning of July 26th, 2012. The electrical discharge blew the chimney wall creating two significant openings. After the accident, an intervention was planned and executed to reestablish its sound structural condition. Before and during the structural works, a SHM plan was put into practice. Several ND tests were performed aiming at evaluating the loss of stiffness and mass, as well to control the execution of the rehabilitation works. A permanent dynamic identification system was installed in the chimney and the evolution of modal properties was followed during the site works. A FE model was built and calibrated with inverse problems to the two structural conditions. Finally, all the information was merged to evaluate the effectiveness of the rehabilitations works. The paper presents detailed information about the damage, the two structural conditions before and after the intervention works and the SHM results.
Visualized Steel Cable NDE Technique Using Multi-Channel MFL Sensor
A visualized cable monitoring system is proposed that uses a magnetic flux leakage (MFL) -based nondestructive evaluation (NDE) technique and a cableclimbing robot to detect the local faults (LF) of steel cable in long span bridges. To verify the feasibility of the proposed damage detection technique, a multi-channel MFL sensor head was designed and fabricated. A steel cable specimen with several types of damage was fabricated, and scanned by the fabricated MFL sensor head to measure the magnetic flux density of the specimen. To interpret the condition of the steel cable, measured magnetic flux signals were used to determine the locations and the levels of damage. Measured signals from the damaged specimen were compared with thresholds that were set for objective decision-making. In addition, the measured magnetic flux signal was visualized as a 3D MFL image for convenient cable monitoring. Visualized results were compared with information on actual inflicted damages, to confirm the accuracy and effectiveness of the proposed cable monitoring method.
Online Real-Time Corrosion Monitoring Technology Selection for a SHM Pilot in Gathering Lines: A Case Study at Ecopetrol's Oil Production Facilities
This paper describes the process used to determine the most suitable set of online real-time pipeline corrosion monitoring technologies as the core for Structural Health Monitoring pilot project at an oil production facility in the region of Meta, Colombia for Ecopetrol S.A. This method was used and followed for a particular case on gathering lines and transporting the production fluids (mean BSW 80%) with an internal corrosion as the main damage mechanism. The criteria used for the decision making was a technology road mapping elaborated specifically for internal corrosion monitoring which lead to a total of 33 related commercial products for both direct (pipe wall thickness estimation) and indirect (fluid corrosivity) damage estimation. These products are based on normal ultrasonic waves, guided ultrasonic waves, fiber optic, field signature method, electrical resistance, linear polarization resistance and coupled multi-electrode array sensors among other technologies. By using a set of initial criteria regarding the infrastructure conditions and pilot desired architecture like temperature range, communication protocol, pipe diameter, alarm configuration and DCS integration capacity, several products were dismissed leading to a smaller set of eligible technologies to choose from, finally it is worth to state that the objective of the SHM pilot is to evaluate the technical viability as well as the affordability of spreading the SHM practices (widely explored and documented for aerospace and civil industry) in production oilfields at Ecopetrol S.A.
Integration of Fiber Optic Platform and Electro-Mechanical Sensors to Enhance Damage Detection Process
The Structural Health Monitoring (SHM) is an ongoing research topic and SHM systems have become indispensable part of a new construction in recent years. In this paper, an effective SHM system consists of a traditional data acquisition platform along with a fiber optic platform is used to enhance damage detection process. The SHM system is applied on a model steel truss bridge that can present different damage scenarios. The traditional data acquisition system consists of piezoelectric accelerometers sending vibration signals from the traffic on a bridge to a NI data acquisition device. The second system uses Fiber optic Bragg Grating (FBG) sensors which send signals to a fiber optical interrogator. This system connects to a computer via Ethernet and can be used to send signals wirelessly. The installed SHM system takes advantage of benefits of piezoelectric sensors that are easy to be installed and are sensitive to the vibration. The traditional data acquisition device can be programmed based on the user needs via Lab-View platform. Moreover, the SHM system benefits from FBG strain gauges and temperature sensors that have high resolution and high signal to noise ratio. The shifting in the central wavelength of the Bragg gratings is the strain transduction mechanism in FBG sensors. Using the two platforms, we will present a significant enhancement in damage detection process. The installed SHM system tries to find all possible damage scenarios in a model structure similar to real bridges allowing authorities to take precautions to prevent further damage.
A Structural Health Monitoring Approach for Damage Detection in Wind Turbine Blades Based on Compressed Sensing Acquisition of Acoustic Emission Events
The wind power capacity installed over the U.S. is growing at a fierce pace. According to American Wind Energy Association reports, between 2007 and 2010, wind power provided more than 35% of all new U.S. electric capacity. Despite the rapid growth in wind energy, there are still significant wind energy resources that are underutilized. The U.S. Department of Energy estimates that offshore wind alone could offer over 4,000 GW of electrical energy which is four times the nation’s current total generation capacity. In order to harness this renewable resource significant logistic challenges must be overcome to keep these systems working in harsh offshore environments. The deployment of a structural health monitoring (SHM) systems will be important for these off-shore wind power plants, which are gaining an increasing interest by investors. Wind turbines come in a number of typologies (e.g. horizontal-axis wind turbine and vertical-axis) and are selected according to the specific environment in which they are designed to operate. Furthermore, wind turbines are complex cyber-physical systems consisting of blades, generators, hydraulics and electronics/computation each of which features different failure mechanisms with unique impact in term of down-time and repair cost. All these systems must be continuously monitored in order to ensure they are operating to their maximum potential. Unfortunately continuous monitoring using conventional analogto- digital converters will result in a large amount of data that will need to be stored and processed. In this work we study the suitability of compressed sensing (CS) for long-term acoustic emission (AE)-based SHM of wind turbine blades. In particular the current work simulates the CS acquisition process as applied to AE data acquired during experimental fatigue-to-failure tests (see Figure 1) performed on a full-scale blade of a horizontal axis wind turbine (HAWT). AE represents a promising technique to develop a monitoring strategy for detecting and possibly localizing the presence of damage (i.e. delamination) in mechanical components. When damage occurs or propagates in a structure an acoustic wave is generated by the rapid release in the internal stress of a material. These AE events can be captured at a relative large
Probabilistic Risk Prediction for Aircraft Fatigue Life Management Using SHM Systems Considering the Effect of Inspection Correlation
A numerical study was conducted to investigate the effect of inspection correlation of a SHM (Structural Health Monitoring) system on the risk prediction. The study was based on a new Probabilistic Structural Risk Assessment framework which accounts for statistical correlations among inspection outcomes from multiple SHM sensors mounted on the same structural component. The core of this framework is an innovative, computationally efficient probabilistic method termed RPI (Recursive Probability Integration). RPI is suitable for damage tolerance and riskbased maintenance planning using inspection results from either NDI (Nondestructive Investigation) or SHM systems. The RPI methodology is capable of incorporating a wide range of uncertainties including material properties, repair quality, crack/damage growth related parameters, loads, probability of detection, and inspection correlation. The result of the study demonstrates that: (1) the inspection correlation has significant effect on the risk prediction and cannot be ignored, (2) relative to independent inspections commonly assumed, the inspection correlation has a negative impact to structural risk, and (3) the stronger the inspection correlation, the larger the risk the structural component may experience.
Selection and Identification of a Shape Function for Modeling Degradation as a Gamma Process
This paper proposes an approach for estimation of Remaining Useful Life (RUL), in which data from earlier lifetimes is utilized in the identification of the functional shape of degradation. A set of candidate shape functions are identified, and one of them is selected for RUL estimation, and consequently for inference, based on information theory. The feasibility of the approach is studied using functional forms of degradation acquired through air filter loading experiments and air quality measurements representing degrading factors. The study indicates the usefulness of utilizing data from previous lifetimes when reliable enough a priori information of the degradation phenomena is not available. The observed improvement in accuracy was considerable at long prediction horizons, which is beneficial especially in ConditionBased Maintenance applications (not limited to air filters) with infrequent maintenance shutdowns.
Continuous Monitoring of an Aging PC Bridge by Using Fiber-Optic Strain Sensors Based on Time-Series Analysis in Considering Environmental Effects
This study is aimed to construct a continuous structural health monitoring (SHM) system for an existing prestressed concrete (PC) bridge, in which the fractures of PC cables were found. This is why the structural condition monitoring is required for assuring safety use until the replacement. The fiber-Bragg grating (FBG) sensor network for acquiring multipoint strain data was then installed, and two kinds of data acquisition were carried out. The first one was the static loading tests, which were performed before, after, and one year after the reinforcement of the bridge. The second one was the continuous data acquisition from December 2011 to November 2012. In this paper, we firstly show the comparison of strain data acquired in the loading tests, and indicates that it is difficult to reveal the structural condition and its changes only by the periodic data acquisition. The continuous monitoring data consist of multipoint strain data that were acquired every one hour for eleven months. It was clearly recognized that the time series of strain showed high correlation with the temperature, in addition, the possibilities of correlations with the sunshine duration and the snow accumulation in winter were also indicated. To analyze those environmental effects and extract the structural condition, we then considered applicability of the time-series analysis. It was then shown that the trend and variations of strain data could be reconstructed by applying the seasonal autoregressive integrated moving average (ARIMA) model.
Fatigue Damage Prognosis of Single-Edged Notch Beam Using Piezoelectric Transducers
Fatigue cracks usually initiate at points of high stress concentration in structures subjected to cyclic loads. This could happen even when the loads are less than the strength capacities of the structures. A structure may be rendered unserviceable with the onset and detection of a fatigue crack. For applications that could tolerate stable fatigue crack growth, the structure can still remain in service, but the integrity of the structure needs to be reassessed and the remaining life of the structure needs to be determined. The field of damage prognosis has evolved to address this need for estimation of remaining life, so as to facilitate the decision making process by the stakeholders and to prevent sudden failure of the structure. This paper aims to estimate the remaining fatigue life of laboratory-sized aluminum alloy specimens by incorporating the uncertainties in material fatigue properties, such as the Paris coefficients, and the errors in the estimation of crack length using piezoelectric transducers with the Lamb wave propagation technique. Fatigue tests are conducted on aluminum single-edge notched specimens to determine the Paris coefficients (m and C) and their deviations. In actual applications, when the damage index is obtained, the crack length can be estimated according to its linear correlation with the damage index, which is defined as the ratio of the reduction in signal amplitude to the baseline signal amplitude. A Monte Carlo simulation is then performed to simulate the fatigue crack growth using linear elastic fracture mechanics for 2000 samples. The fatigue life for the 2000 samples is extracted and the mean is used as the estimated fatigue life of the specimen. There is good agreement between the estimated remaining fatigue life and the actual remaining fatigue life.
Identifying Pipe Degradation in a Highly Dynamic Environment Using Singular Value Decomposition
Pipe corrosion is a common degradation that can cause leakages, breaks, or even explosions. Detecting and monitoring corrosion is necessary to ensure to the structural integrity of pipes. Ultrasonic guided wave techniques are one promising solution due to their sensitivity to small defects and capability to propagate long distance. However, it is difficult to interpret pipe guided wave signals directly or to extract effective corrosion-sensitive features from them due to their multi-path, multimodal, dispersive characteristics. Moreover, pipe structures often experience large variations in environmental and operational conditions (EOC), which can produce large changes in ultrasonic signals and can easily produces false alarms. In previous work, we developed a robust damage detection method based on singular value decomposition (SVD). In this paper, we further explore, through experiments, the capability of our SVD-based method to detect degradation. We demonstrate that our method is effective at identifying progressive corrosion at the same time that large temperature variations are present.
A Proposal for Exteriori-Interiori Integrated Structural Health Monitoring Framework
The motivation of this study is to determine a technique to completely describe the damage state of large deformed structures commonly found during forensic investigations. The combination of Laser Detecting and Ranging (LiDAR) and Piezoelectric (PZT) sensing technologies for damage quantification is suggested to generate a full-field description of a permanently deformed plate. The test subject is a 16 inch by 16 inch aluminum plate subjected to different damage scenarios. LiDAR is a static scanning laser that provides a 3-dimensional picture of the object. Smart Layer is a commercial PZT actuator/sensor network system that generates stress waves for internal damage evaluation. Both techniques have been applied to the test plate after damages were introduced. In order to effectively analyze the results, the images for each test were superimposed. Four damage scenarios were imposed on the aluminum plate including saw cuts at different depths using an electric saw. The final damage is a severe bending of the plate. The bending of the specimen produced an image that located the most severe damage directly under the left hand portion and directly above the right hand portion of the bend. We propose a structural health monitoring framework, EII-SHM (exteriori – interiori integrated structural health monitoring) that can completely quantify damage on a severely deformed structure. The EII-SHM framework involves workflow in conducting a forensic investigation where exteriori scans using scanning LiDAR is first conducted followed by interiori scan using piezo sensor network. The lidar scan can help determine critical locations of the piezo sensors, hence, optimize sensor placement and number of sensors.
Multi-Sensing Strategy for SHM in Civil Engineering: Case Study of a Structural Element with Carbon Fiber Reinforcement
Working on a concrete beam with a carbon fiber reinforcement, we created an experimental bench that allows us to observe both static deformations and vibrations of the concrete beam under test. Using three sensors types : an accelerometer, a piezoelectric wafer and a strain gage, we are able to observe vibrations up to 20kHz. The sensors complement each other in terms of bandwidth. Using stimuli following different axis, we were able to dissociate the different frequencies present in the oscillations and correlate them to different bending moments. Furthermore, we were able to observe the propagation of the signals across the beam and determine the propagation speed of the signal.
Development of Smart Pultruded Composite Materials with Embedded Fiber Bragg Gratings for In-Situ Strain Monitoring
The major benefits of using composite materials as civil engineering structures are high strength, lightweight, high corrosion resistance, high formability and tailoring. Adding functionalities into composite materials, resulting in so-called smart structures, is a promising alternative to conventional methods for structural health monitoring. A large smart composite platform (20m x 3.5m) was built in september 2012 (DECID2 project). All sub-structures of the platform are made by pultrusion which is a manufacturing process for producing reinforced composite structural shapes of continuous lengths with constant cross-sections. Two composite girders (I-beam cross section) are used to support small composite beams on which precast composite deck have been assembled. The monitoring of this structure is based on Fiber Bragg Gratings (FBG) which are used as strain and temperature sensors. This kind of sensor exhibits numerous advantages such as small size, relative lightness, an immunity to electromagnetic interference, geometrical flexibility and so on. In our project, FBG sensors are embedded in composite girders during fabrication. The embedment of optical fiber sensors in pultruded composite materials was one of the key- technical challenges. The ingress and egress of optical fiber from a composite component made by pultrusion are real issues which are difficult to solve. Indeed, ingress and egress of optical fiber must be enough robust for the successful, cost-effective development of embedded sensor technology. In our project, we have developed and tested a miniaturized connector which can be embedded in composite component during pultrusion process. This connector can withstand the harsh conditions (high temperature and shear stresses) of the pultrusion process. We will also discuss the mechanical performance of optical fiber sensors while embedded in pultruded materials. Quasi-static and fatigue mechanical tests have been performed on composite specimens. The results show that embedded FBG sensors could monitor strain with a good precision although a slightly decrease of fatigue performance of smart composite specimens was noticed.
Multidisciplinary Health Monitoring of a Steel Bridge Deck Structure
Fatigue cracks in orthotropic bridge decks are an important cause for the necessary renovation of existing bridges. Parallel utilization of various technologies based on different physical sensing principles can potentially maximize the efficiency of structural health monitoring (SHM) systems for these complex structures. A multidisciplinary SHM system composed of acoustic emission, strain measurements, and vibration measurement has been applied to a test steel bridge deck under a fatigue loading in a lab environment. Having the main focus on acoustic emission and strain measurement, the present paper provides an evaluation of the performance of the SHM system in terms of detection of the crack initiation, localization, and growth. During the destructive test, the strain measurement was employed as a local monitoring method, and acoustic emission was utilized as a local/global monitoring scheme. The monitoring performance was validated using visual inspection and timeof- flight diffraction (TOFD) measurements. Some of the main observations in this study are: (i) good agreement between the estimated crack size based on the strain measurement and TOFD measurement, (ii) the earlier detection of the crack activity by acoustic emission compared to the 10% deviation of the strain measured at a few millimeters away from the crack initiation location, and (iii) localization of multiple active cracks by acoustic emission.
Application of SHM Using an Autonomous Sensor Network
Assessment of the condition of a structure in operation and subsequently its remaining maintenance free service time, is of increasing importance for various fields of engineering. The main drivers for this are cost effectiveness, increased system reliability, system safety and reduced environmental impact. The current generation of monitoring systems relies on active, power intensive excitation and wired communication. Systems based on operational vibrations of the structure and employing a network of smart, autonomously operating and wireless sensors offer new possibilities, but also pose new constraints. Damage identification methods are therefore sought that combine local low power usage and low data transmission with a high reliability. The focus of this paper is on operational vibrations and modal based Structural Health Monitoring damage identification methods, applied in large civil structures such as wind turbine towers and bridge decks and, to a lower extent, in large composite structures. Three methods are compared, both experimentally and numerically: Peak Picking (PP), Random Decrement – Frequency Domain Decomposition (RD–FDD) and Random Decrement – covariance based Stochastic Subspace Identification (RD–SSIcov). The RD–FDD method is found to be a suitable method for modal based damage identification, given the restrictions on smart wireless sensor networks.
An Experimental Study on Damage Detection of a Building Structure Model
The collapse of a large infrastructure such as bridges, dams and harbors cause serious losses. Therefore, there has been an increasing demand in using Structural Health Monitoring techniques for the building structures so that their maintenance cost and time can be reduced. In this study, a structural health monitoring methodology using dynamic responses is proposed for damage detection of a prototype building structure during shaking table testing in different positions of structural damage. Damage characters are calculated from the measured acceleration data and applied to outlier analysis, one of unsupervised learning based pattern recognition technique for a damage index and a decision making. And a threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. For more systematic damage detection, several control parameters to determine and optimal decision boundary for the unsupervised learning based pattern recognition are optimized. Finally, further research issues will be discussed for realworld implementation of the proposed approach.
Global Assessment of a Cable-Stayed Bridge Model Using SNLSE Approach
Global assessment of structural conditions is important for structural health monitoring system. In particular, online or almost online structural parametric identification, based on vibration data measured from sensors, has received considerable attention recently. However, the problem becomes more challenging when the structure is complex and the number of degree-of-freedom (DOF) is large. A newly proposed time domain analysis methodology, referred to as the sequential nonlinear LSE (SNLSE) approach, has been studied and shown to be useful for the online tracking of parameters for structures with small DOFs. In this paper, the SNLSE approach will be applied for global assessment of an experimental cable-stay bridge model with large DOFs. A dynamic equivalent model of the bridge will be established and finite element analysis will be carried out to formulate the equation of motion. Numerical analysis will be conducted with different simulated damage scenarios and limited number of response data is considered. The capability of the proposed SNLSE approach in identifying the structural parameters and assessing the structural conditions will be verified.
Numerical Simulation of PZT Bonded Reinforcement for Health Monitoring of Reinforced Concrete Structure
Structural Health Monitor (SHM) for concrete structure using PZT is under wide interests and investigations. Due to complexity of concrete microstructure and the electric-mechanical coupling effect of PZT, the numerical simulation turns to be a good choice for providing theoretical guidance for study and analysis of such an SHM system. An active sensing diagnostic system for SHM of reinforced concrete has been currently under the investigation. Test results show that the system can detect damages of the structure. To fundamentally understand the damage algorithm and therefore to establish a robust diagnostic mechanism, an accurate Finite Element Analysis (FEA) for the system has been performed. A steel reinforcement bar with surface bonded PZT under a transient wave load is simulated using commercial FE software ANSYS. In the model, the rebar with PZT and epoxy layers is built using 2D axisymmetric type. PZT material property transformation is discussed due to format differences between IEEE Standard and ANSYS. The analysis uses the direct coupled-field analysis module. And material properties such as the Raleigh damping coefficients are discussed. The numerical model has been validated with the experimental testing. The good consistency between simulation and test shows that the model is reasonably accurate. This provides a base for further system optimization. A potential signal improvement method is explored. By changing PZT layout into different configuration, the output signal is increased four to six times larger than the original one.
Advancements on the Inspection of Underwater Plates by Means of Guided Waves
This paper presents the results of an experimental study where guided ultrasonic waves (GUWs) are used for the structural health monitoring / nondestructive evaluation of immersed plates. The method hypothesizes the generation of leaky Lamb waves by means of laser pulses and their detection by an array of immersion transducers. The signals are processed using continuous wavelet transform to extract few damage-sensitive features. These features are then used to an outlier analysis problem and to a probabilistic approach to create an image of the plate in search for possible structural anomalies. In the study presented in this paper, an automatic smart scan system designed and built at the University of Pittsburgh is employed to inspect underwater plates reliably and repeatedly. The time waveforms associated with all the laser-irradiated point to sensor paths are processed using the algorithms proposed here. The results indicate promise and future studies should focus on the generalization of the methodology.
Damage Detection and Localization Using Lamb Waves in Highly Anisotropic Structures with Multiple Curvatures
For the development of structural health monitoring (SHM) systems and nondestructive testing methods based on Lamb waves it is very important, to detect and visualize the entire wave field in the structure. This is currently the only way to reliably determine the source of the waves detected at SHM sensors and to trace their propagation paths in complex structures. During the past years non-contact methods using air-coupled ultrasonic sensors were successfully developed at the DLR to visualize Lamb waves [31]. However, on the way from laboratory applications towards a practical use of SHM systems using Lamb waves there are still many more obstacles to overcome. This paper addresses the special challenges, that arise for the detection and evaluation of wave fields due to the shape and the material properties of real fiber reinforced structures compared to standard flat plates used in the laboratories. An optical measurement platform and a special reconstruction algorithm for damage localization are presented. The measurement system and the algorithms are especially focused on applications for large, curved structures like airframe components and wind turbine rotor blades.
Opportunities for Structural Health Monitoring of Composite Material Structures with Novel Microstructured Optical Fiber Sensors
The possibility of embedding optical fiber sensors inside carbon fiber reinforced polymer (CFRP) for structural health monitoring purposes has already been demonstrated previously. So far however, the sensitivity of these sensors to transverse load (i.e. out-of-plane load) remained considerably lower than that to axial strain. The design flexibility provided by novel microstructured optical fiber (MOF) technology now allows developing dedicated fibers with substantially enhanced sensitivity to transverse load. We exploited that flexibility and we developed a MOF that, when equipped with a fiber Bragg grating (FBG), leads to a sensor that allows measuring transverse strains in reinforced composite materials, with an order of magnitude increase of the sensitivity over the state-of-the-art. This is confirmed both with experiments on such fibers embedded in CFRP coupons and with finite element simulations. Our sensor brings the achievable transverse strain measurement resolution close to a target value of 1 microstrain (Î¼ï¥) and could therefore play an important role, not only in the domain of structural health monitoring, but also in the field of composite material production monitoring.
Guided Waves in a Disbonded Honeycomb Composite Structure
Damage identification using ultrasonic nondestructive evaluation (NDE) requires a good understanding of the properties of the various types of waves that can be transmitted in the structure in presence or absence of damage. For successful application of these techniques to locate and estimate the severity of the damage, it is extremely important to understand the propagation characteristics of ultrasonic waves in these structures. Wave propagation in composites is extremely complex due to material inhomogeneity and anisotropy, where characteristics of the waves depend on the laminate layup, direction of wave propagation, frequency, and interface conditions. When elastic waves are generated by surface sources in a plate, they experience repeated reflections at the top and bottom surfaces alternately. The mutual interference of the reflected waves results in propagation guided by the plate surfaces. In this paper a specific structure will be analyzed with different levels of complexities as far as the wave propagation characteristics are concerned. The structure is a sandwich plate composed of two carbon-epoxy face sheets with an aluminum honeycomb core with hexagonal cells. The work is carried out using theoretical analysis, numerical models and experimental verifications. Numerical (Finite Element) models are used for more practical cases, for which the geometric and material complexities of actual structures present practical difficulties in direct analysis of wave propagation data using theoretical constructs only.
Compressive Sensing for Damage Detection in Composite Aircraft Wings
This work deals with an ultrasonic guided wave structural health monitoring (SHM) system for composite aircraft wing damage inspection. The main idea is to use piezoelectric discs bonded to various parts of the aircraft wing, in a form of relatively sparse arrays, to generate and receive ultrasonic guided waves aimed at detecting defects. The development of a structural monitoring system able to inspect large composite structures and to communicate remotely to the central unit is challenging due to both the huge number of piezoelectric sensors needed and the high sampling frequency of the recorded signals. To address this problem, here a low rate sampling system has been developed by using a modified version of Compressive Sensing (CS) technique that exploits the sparse representation of dispersive and anisotropic ultrasonic guided waves in the frequency warped basis. The CS framework is applied to lower the sampling frequency and to enhance defect localization performances in composite material through the analysis of anisotropic guided waves propagation. The approach is based on the inverse Warped Frequency Transform (WFT) as the sparsifying basis for the CS acquisition and to compensate the dispersive behavior of guided waves for several different directions of propagation. As a result, an automatic detection procedure to locate defect-induced reflections has been successfully tested on Comsol software simulated guided waves propagating in an composite wing specimen.
Ultrasonic Guided Wave Based SHM of a Steel Shell Structure Under Different Operating Conditions
The use of multi-element strip transducers to provide control of the generated ultrasonic guided wave mode in a structure is described. The transducer is conformable to curved surfaces even though it makes use of a piezoelectric fiber composite. The ability of the transducer to generate non-leaky guided waves in a steel mockup structure representative of a pressure vessel is shown, and its capability is compared to that of piezoelectric wafer active sensors.
Continuous Mode Conversion of Lamb Waves in Carbon Fibre Composite Plates—Occurrence and Modelling
Recent experimental investigations with scanning laser vibrometer showed irregularities in assumedly undamaged CFRP plates with transiently excited symmetric (S0) and antisymmetric (A0) wave groups. The faster S0- and slower A0- waves occur and move as expected, but arising waves between the primarily excited symmetric and antisymmetric wave groups represent an anomaly in wave behaviour. Since these waves turned out to be A0-waves and extend over an area, they indicate quasi “continuousâ€ mode conversion. The paper includes explanations about why and where this phenomenon occurs. Furthermore, suggestions for the modelling of the “continuousâ€ mode conversion in CFRP plates, using 2D spectral finite elements in the time domain for the plane strain state, are presented.
Ultrasonic Guided Wave Propagation in Cellular Composite Plates
Using guided waves (GW) generated and received by a mesh of piezoelectric (PZT) actuators and sensors is a developing technology for structural health monitoring (SHM) in composite structures. Due to the high sensitivity, the possibility of an online monitoring and the low cost of the required equipments, GW based damage detection systems are an interesting SHM technique for many industries. In the present study, the application of GW for SHM has been extended to more complex composite cellular structures, such as honeycomb, metallic foam, hollow sphere and particle rein-forced structures. To avoid the experimental costs numerical simulations based on finite element method (FEM) are applied to investigate these phenomena. To reduce the computational efforts a new kind of simplification approach based on geometrical parametric studies is suggested and tested numerically as well as experimentally. Finally, based on the parametric studies the appropriate mode and frequencies are suggested to detect damages in cellular structures. The damages are determined based on mode convergence phenomena as the waves interact with damages in cellular composite structures.
In-Situ Guided-Wave-Based Health Monitoring for Train Bogie Structures: Technique Development and Application to Beijing-Shanghai High-Speed Railway
An in-situ online health diagnosis and prognosis system (OH-DPS) for train bogie structures was developed. The system, residing on a PXI platform, was recently installed on China’s latest high-speed train model CRH380CL. An active piezoelectric sensor network was permanently attached to the bogie frame of the train to actively actuate and receive ultrasonic guided waves. A mass of signals acquired via the system was processed, from which a wide array of signal features was extracted and fused by the built-in program, to monitor the health status of the bogie in a real-time fashion. During the train’s conformance testing on the BeijingShanghai High-Speed Railway, continuous health monitoring of the bogie frame was carried out, covering a variety of events including departure, speed-up, full speed operation (300 km/h), emergency brake, track change, and stop. Mock-up damage added to the bogie’s side panel was successfully located by the system during the operation, and the detection results were visualized in a rapid manner through a diagnostic imaging algorithm. The in-situ experiment has evidently demonstrated the feasibility, practicality, effectiveness and accuracy of guided-wave-based health monitoring for high-speed train bogies under real-world working conditions.
On the Structural Health Monitoring of Repaired Aerospace Structures
A major challenge of aircraft manufacturers is to ensure the operational life extension of ageing aircraft with an affordable maintenance cost. Composite adhesively bonded repair patches are a promising approach which could meet the increasing aerospace industry demands for aircraft maintenance in an efficient and relatively easy to apply manner. Structural health monitoring (SHM) techniques are an essential part of the design of a reliable and robust repair system. In-situ continuous damage assessment of the repaired region could provide information about the integrity of the repaired structure when it is subjected to mechanical and thermal loads, hence eliminating unscheduled maintenance and unnecessary inspection costs. This work aims to investigate the key challenges in different repaired configurations in terms of health monitoring through guided ultrasonic waves. Two representative composite repairs are tested, namely a scarf patch repair and an external bonded patch, while their structural integrity are assessed under static and cyclic loads, respectively. The panels are interrogated with Lamb waves that are actuated and captured by surface bonded piezoelectric transducers. The investigated techniques aim to characterise the integrity of the repairs by means of pattern recognition and data dimension reduction techniques such as outlier analysis and principal component analysis. In addition, the applicability of the proposed methods is demonstrated on an aluminium helicopter’s stabilizer in which a pre-introduced crack is repaired with a composite patch and the demonstrator is subjected to bending fatigue. Each case study utilised alternative nondestructive techniques for validation such as 3D digital image correlation (DIC) and X-ray radiography. It will be shown that the obtained results successfully identify the presence of damage, hence providing a potential tool for built in on-line monitoring of adhesively bonded patch repairs.
Signal Processing System for Guided Wave-Based SHM Technique
Reported research is focused on practical realization of guided wave-based SHM. Miniaturization of a SHM system could be very interesting in the scope of real life application. Embedded solution allow to minimize the dimensions of the whole system replacing a PC computer for signal processing. In this research damage localization system was developed. Damage localization process is based on elastic wave propagation method. Embedded system was realized on Field Programmable Gate Array FPGA in which system on programmable chip (SoPC) was implemented. Damage localization algorithm was coded in C++ and implemented in this system. Elastic waves were generated and received using dedicated device. Gathered signals representing elastic waves propagating in structures were sent to embedded damage localization system in order to extract damage location. The aim of developed damage localization algorithm is to create colour map that indicates places of elastic waves reflections. Wave reflections are caused by all kind of discontinuities existing in the monitored structures like: boundaries, transducers and most important – damage. In the first approach this algorithm was coded in MATLAB® environment. This software allow to very fast coding of developed algorithms because of large number of functions and libraries included in MATLAB®. The huge disadvantage of this environment is the long time of calculation if we compare it with the code developed in C++. Second thing is a price connected with MATLAB® purchase. In this purpose MATLAB® script code was converted to C++ code with graphic user interface GUI working under the PC with Windows operating system in order to verify the results. In next step C++ software was developed in order to embedded it in post processing unit. Thanks to this the result of damage localisation is displayed on the LCD display of the system. The reported research led to development of an embedded system for signal processing in SHM applications.
Nonlinear Guided Waves for Thermal Stress Measurement in Constrained Solids
Nonlinear guided waves have drawn considerable interest in the last few decades for interrogating long waveguide-like structures as they efficiently combine high sensitivity to peculiar structural conditions, typical of nonlinear parameters, with large inspection ranges, characteristic of wave propagation in confined media. Their mathematical treatment, however, becomes extremely challenging when characterized to waveguides that are complex in either material and/or geometry. Therefore the successful use of nonlinear guided waves as a structural diagnostic tool is not always a trivial task. The classical mathematical framework governing nonlinear wave propagation in solids relies on finite strain theory. The present work presents a constitutive model to expand the topic of nonlinear wave propagation to the case solid under constrained thermal expansion. The origin of nonlinear effects in this case is explained on the basis of the anharmonicity of interatomic potentials, and the absorption of the potential energy corresponding to the (prevented) thermal expansion. Such “residualâ€ energy is, at least, cubic as a function of strain, hence leading to a nonlinear wave equation and higher-harmonic generation. The model predicts a decrease in longitudinal wave speed and a corresponding increase in nonlinear parameter with increasing temperature, as a result of the thermal stresses caused by the prevented thermal expansion of the solid. This theoretical framework is applied for the prediction of thermal stresses in Continuous-Welded Rail (CWR). The almost total absence of joints makes these structures prone to breakage in cold weather and, more importantly, buckling in hot weather. Field tests were performed at the Transportation Technology Center (TTC) in Pueblo, CO. The collected results suggest the potential of the proposed nonlinear ultrasonic measurement technique to quantify thermal stresses from prevented thermal expansion and prevent catastrophic failures for buckling-prone structures.
Analysis of Wave Propagation in Rib-Stiffened and Isogrid Panels for Structural Health Monitoring
Practical satellites used for commercial and defense applications are very complex. Simplified approximations to satellite panels and components will yield undesirable errors in many damage identification techniques, particularly those that consider time of flight for elastic waves. Rib-stiffened and isogrid panels are such examples of structural satellite components. Preliminary evaluations of health monitoring techniques which relied on a simple flat plate assumption revealed unsatisfactory results for analysis of rib-stiffened structures. Further investigations have been made into the effect which ribs have on wave propagation. A testbed structure was fabricated to include several characteristics of a realistic satellite, including a skeletal structure with isogrid panels secured by threaded fasteners. Several wave propagation runs were taken on the structure through various ribbed paths. Finite element models were developed to explore the nature of waves passing through ribs in both 2D and 3D representations. The investigation resulted in identification of converted wave pulses. The work has lead to the proposed „modified group velocity‟ for calculating the distance to damage based on wave pulse time of arrival.
Progress in Structural Health Monitoring Technology and System for the Large Crane in China
In recent years, the crane moves toward large-scale of structure and diversification of functions. The operating frequency of the large crane is rose, and the phenomenon about overload operation of the large crane is increased. The number of large crane accidents is also shown ascending trend. In order to ensure safety and reliability of the large crane running, a series of achievements about structural health monitoring technology for the large crane were carried out by Structural health monitoring team of China special equipment inspection and research institute in recent years. And a series of achievements with high project application value are acquired. The research achievements about the health monitoring technology based on fiber optical sensor, the local damage monitoring technology based on fiber optic acoustic emission sensor, the structural health monitoring technology based on wireless strain sensor and the project application are introduced briefly in this paper. The structural health monitoring technology for the large crane can not only realize real-time monitoring to the structural strain and the damage status, but also can get the failure precursor about the crane structure. It can prevent sudden disaster, and avoid heavy casualties and property losses.
A Novel Time-Domain Technique for Damage Detection Applied to SHM Using Savitzky-Golay Filter
This paper presents a novel damage detection approach for Structural Health Monitoring (SHM) systems, which is based on Savitzky-Golay filter and time-domain analysis. In this technique, a specific PZT (actuator) excites the structure and three other ones are used as sensors to receive the structural responses. This method is quite similar to Lamb wave based methods, but the excitation procedure and the analysis of the response signals are different. For the proposed method, a wide band chirp signal, with low amplitude and variable frequency, is used to excite the structure. The response signals are acquired in time-domain and smoothed by a Savitzky-Golay filter, and from the filtered signals the first and second derivatives are computed. CCDM (Correlation Coefficient Deviation Metric) indices are calculated from all signals (smoothed, first and second derivatives), considering the structure a healthy condition as a reference (baseline). Practical tests were carried out on an aluminum plate and the results have demonstrated the effectiveness of the proposed method.
Quantitative Crack Monitoring Using Diffracted Lamb Waves
Due to cyclic loading and longtime exposure under extreme environmental conditions, fatigue cracks are often generated in aircraft structures. When the crack grows to a critical size, the structure will not be able to bear the loads, and the structure may rupture suddenly, thus the crack will cause great damages to the aircraft. To evaluate the severity of the crack, one has to know not only the location, but also the size of the crack. Therefore size monitoring of fatigue cracks in aircraft structures is very important to manufacturers as well as maintenance personnel. Some nondestructive evaluation (NDE) methods can be used for this purpose, such as X-ray, Cscan and magnetic particle testing, however, these techniques is limited to ground testing. Many methods have been developed for crack size monitoring and quantification by sensors mounted on the structure in the past few years. However, most of these methods demand dense sensors or sensor arrays around the crack. So crack size monitoring is still a very challenging problem. In this study, Fastest Time of Flight Diffraction (FTOFD) method was developed to monitor the location and size of the crack. FTOFD method uses a sparse sensor network to activate and receive Lamb waves in a structure. Diffraction waves will be generated at the crack tips when Lamb waves pass the crack. Diffraction waves are then received and analyzed by signal processing methods to get the position of the crack tips. Once two crack tips of the crack are positioned, both the location and size of the crack can be given very conveniently. An aluminum plate was used to verify the FTOFD method. A simulated crack was made on the plate by a wire-electrode cutting machine. Experimental results showed that the monitored size of the simulated crack is very close to the real size of the crack.
Active Structural Integrity Monitoring of the Aircraft Based on the PZT Sensor Network—The SYMOST Project
This paper presents an approach to the health monitoring of the PZL–130 TC II Orlik aircraft structure during the full scale fatigue test (FSFT). The results of the final stage of the project which main aim was to develop a system for the aircraft structure monitoring will be presented. A network of PZT piezoelectric transducers was assembled on the aircraft structure and a dedicated hardware enabling remote control and programmed measurement performance was elaborated. A multichannel acquisition system of large dataset of measurements from the ‘hot-spot’ locations of the sensors on different stages of the fatigue tests is described in the paper. The system components are: remote monitoring unit, signal analysis, graphical user interface, sensor self-diagnostic tools, and data classification model. During the test of the aircraft, fatigue cracks were found and verified based on a detailed NDT schedule as well as Resistance Crack Gauges (RG) at particular locations. In the adopted approach a set of Damage Indices (DI’s), carrying marginal signal information content and correlated with the total energy received by a given sensor are proposed. The paper provides the description of elaborated techniques for the signal processing and inference about the damage presence. In particular some issues concerning the damage growth monitoring are discussed and the so called Averaged Damage Indices (ADI’s) introduced. Damage detection capabilities are exemplified by data collected from selected network nodes where damages were found during the project run and the results of their monitoring will be highlighted. The necessity of the use of NDE assisted technology for early damage detection has been proven within the project, especially for the ‘hard to access’ locations in the aircraft structure.
Improved Sensitivity of Condition Monitoring Features via Hölder Exponent Analysis
This paper proposes the use of a condition monitoring method for detecting damage and wear in rotating machines. The method makes use of the time-dependent Hölder exponent, a measure of signal regularity/continuity, and its power spectral density to improve upon traditional condition monitoring damage features. The proposed method is used for detecting worn tooth damage in gearboxes. A basic introduction behind the theory of computing the Hölder exponent is provided, followed by a statistical analysis approach to classifying damage and comparing the performance of the proposed method to traditional methods in current practice.
Multi-Sensing SMART Layer® Technology for Vehicle Protective Systems
This paper presents the application of SMART Layer® Technology for in-situ assessment of the health of ceramic/composite panels used in protective systems. The work builds on previous studies to integrate sensors into the plate manufacturing process, and addresses issues such as ingress/egress of wiring and sensor survivability. An embeddable structural health monitoring system that could be seamlessly integrated into a semi-automated manufacturing process for protective systems was developed. Multiple sensor types and monitoring modes were integrated, including passive impact monitoring, electro-mechanical impedance measurements for sensor self-diagnostics, active ultrasonic scans for damage detection, and static strain measurements. Both low velocity and high velocity ballistic impact tests were conducted, and results are presented and discussed.
Design Framework for Vibration Monitoring Systems for Helicopter Rotor Blade Monitoring Using Wireless Sensor Networks
The pursue of methods for supporting Structural Health Monitoring (SHM) has been an important driver for the technological innovation in several engineering fields such as wireless communication, sensing and power harvesting. However, despite of the innovative and scientific value of these advances, the adoption of SHM and associated technologies by industry has not occurred at the expected pace. One of the possible reasons for this is the lack of a systematic design process for condition monitoring systems tailored to actual mechanical systems. This paper proposes a design framework for a vibration monitoring system that integrates the predictive maintenance needs with the technological developments on SHM methods and related technologies. The framework involves three stages. The first stage corresponds to the identification of the characterization of the system function and its failure and the expected function of the monitoring system. The next stage aims at decomposing the vibration signal according to the dynamic behavior of the system and associated failure. The last stage corresponds to the technological implementation of the vibration monitoring system. To illustrate the applicability of this framework a case study on the development of a vibration monitoring system for helicopter rotor blades by using an autonomous sensor network is presented.
Substructure Model Updating Through Modal Dynamic Residual Approach
Difference usually occurs between predictions of a finite element (FE) model and experimental measurements from the actual structure. To achieve more accurate predictions, FE models can be updated based on field measurement data. In the past few decades, numerous FE model updating methods have been developed. Nevertheless, when applied to a large structure, most methods are computationally challenging and expensive, because these methods operate on a complex model for the entire structure. To address this issue, a substructure-based updating method is presented in this paper. The Craig-Bampton theory is adopted to condense the entire structural model into a substructure (currently being analyzed) and a residual structure. Dynamic response of the residual structure is approximated using only a limited number of dominant mode shapes. A modal dynamic residual approach is adopted for updating the substructure model. This substructure model updating procedure (through modal dynamic residual approach) is validated by numerical simulation with a 200 degrees-of-freedom spring-mass model. The performance is compared with an updating procedure based on a conventional modal property difference approach.
Hybrid Global Matrix/Local Interaction Simulation Approach for Damage Modeling in Composites
This paper presents a hybrid wave propagation model employing the global matrix method and the local interaction simulation approach. The global matrix method is used to accurately calculate the displacement field produced using a directional piezoelectric transducer. The local interaction simulation approach is used to numerically simulate the wave propagation resulting from the actuator and its interaction with the damage features and boundaries of a plate structure. Results from the hybrid model are compared with experimental measurements for an aluminum plate with damage in the form of a through-thickness hole. The model is then used to simulate guided wave interaction with simulated impact damage in a cross-ply laminate.
Modeling Dislocation Mechanisms of the Acoustic Nonlinearity in Metallic Crystals
The acoustic nonlinearity of dislocations in metallic crystals predicted by recently developed analytic models and discrete dislocation dynamics (DD) simulations are compared with past experiments over a range of externally applied stresses. The variation in the acoustic nonlinearity parameter, β, with glide stress on the dislocations is shown to qualitatively agree with the trends seen in experiments on FCC single crystals. Samples that were subjected to a single plastic load are found to have an initial decrease in β, which is consistent with the prediction of an isolated edge monopole. Fatigued samples aligned well with the behavior of two-dimensional multipolar structures as long β from microcracks is negligible.
2D Numerical Modeling of Pipeline Structures with Non-Axisymmetric Loads and Spectral Elements for Identifying Reliable Sensor Spacing
The stable leak detection in gas pipelines using a continuous monitoring system can be used as an early diagnostic tool to prevent catastrophic failures. Acoustic Emission (AE) method has advantages among other Structural Health Monitoring systems through detecting leaks in real time with long range sensors as well as locating leak positions as multi-dimensional space while sensor spacing is a key to detect the target leak rate reliably. The leak amplitude depending on the pipe condition (e.g. internal pressure, buried or on ground, leak rate) and the attenuation curves are needed to define the reliable AE sensor spacing. In this paper, the attenuation profiles of various dimensions (thickness and diameter) pipelines and frequencies are obtained through efficient 2D finite element modeling. The attenuation profiles obtained from numerical studies are combined with the laboratory scale pipe testing that simulates a range of leak rates and pipe conditions. The classical finite element method along with Fourier expansion and superposition technique is studied in literature in order to analyze axisymmetric solids subjected to non-axisymmetric dynamic loading. The superposition method conjoint with traditional formulation is not beneficial as compared to three-dimensional analyses when the Fourier expansion requires many harmonics to represent the non-axisymmetric load, which occurs in case of concentrated load (i.e. leak). This paper presents an efficient formulation of axisymmetric pipelines with non-axisymmetric loading, specifically concentrated load representing leak in pipelines, using the spectral element method which is a special form of finite element based on the nodal coordinates of Lagrange shape functions obtained from the solutions of the orthogonal polynomials such as Legendre, Chebyshev. The algorithm using spectral element method is developed to model a 2D axisymmetric metal pipe. An efficient mathematical approach for the calculation of stiffness matrix in the case of ring element with rectangular cross section is developed in order to minimize the computational time for each harmonic of tangential Fourier expansion and corresponding deformation field. The paper provides the AE sensor spacing needed for the target leak rates for a range of pipe dimensions and wave frequencies, which can be utilized to define the sensor spacing for a particular pipeline and sensor frequency.
Condition Monitoring for Hydraulic Systems in Rolling Mills Using Unscented Kalman Filter
This contribution describes a predictive maintenance strategy through a modelbased condition monitoring scheme for hydraulic systems in rolling mills, with specific application to the wrapper roll control of a hot strip coiler. For this purpose, a diagnosis strategy is developed using the Unscented Kalman Filter (UKF) technique based on typically available measured signals at the investigated plant. The considered hydraulic system is simulated under the development tool MATLAB/Simulink®. Comprehensive nonlinear modeling is necessary to improve the accuracy of the developed monitoring scheme. Due to the highly nonlinear behavior of the considered system, the UKF is suitable to estimate the hydraulic system states and parameters compared to other approaches, such as the Extended Kalman Filter (EKF). Investigated faults in this work are internal leakage between the two cylinder chambers, raised friction in cylinder, raised hysteresis in valve, and eroded control edges. The main contribution and challenge of the proposed approach is not only to identify the type of the possible occurred fault(s), but also to provide an estimation of the extent of the detected fault(s).
Time Domain Response Surface Model Updating for Nonlinear Structures
Finite element (FE) model updating aims to modify the uncertain parameters of a FE model to improve the correlation between certain analytical response features and their experimental counterparts. One of the proposed methods to solve this optimization problem is to approximate the input-output relationship of deterministic FE models with polynomial functions and estimate the uncertain parameters of the model using these approximated surrogate models, called Response Surface (RS) models. In this study, low computational effort associated with RS modeling is used to extend nonlinear FE model updating through time domain data. For this purpose, with assumption of known input and using least square techniques, RS models are constructed at every time step of the analysis and minimization problem of parameter estimation is solved iteratively in length of the time history of responses of the system. This paper investigates sensitivity of such RS estimates to measurement noise and input frequency in FE model updating of nonlinear structures. Further application of this procedure is investigated in a case study of a steel frame with bilinear material model under seismic loading.
Development of Data-Driven Reduced-Order Models for Complex Nonlinear Systems from Experimental Data
This paper presents an overview of a comprehensive experimental and computational study to develop and evaluate some promising testing procedures and processing approaches to obtain high fidelity, data-driven, model-free, reduced-order, representations of complex nonlinear systems in a format that is convenient for use in computational mechanics studies, control applications, and structural health monitoring investigations. A re-configurable test apparatus was designed and assembled so as to allow the incorporation of a wide class of realistic nonlinearities, both stationary as well as nonstationary, that had nonlinear features (such as polynomial-like, dead-space, saturation, dry friction, hysteresis, etc.) that are widely encountered in the applied mechanics field, at various scales spanning the range from large civil infrastructure systems to micro-electro-mechanical devices. Both parametric approaches (based on adaptive least-squares methods) and nonparametric identification methods (using polynomial-basis functions and artificial neural networks) were used in conjunction with data sets obtained from the nonlinear test apparatus when using deterministic as well as stochastic excitations. The application of the proposed analysis methods to several illustrative nonlinear examples is presented. It is shown that the variety of methods that were evaluated in this study provide a “toolkitâ€ containing a useful collection of efficient methods that can handle a very broad class of problems and situations that are often encountered in the nonlinear structural dynamics field.
Adaptive Multi-Scale Modeling of Structures Under Earthquake Loads
Civil structures are designed using the load and resistance factor design (LRFD) philosophy in most parts of the world which recognizes the uncertainty in the determination of loads and strengths. A specific structure can be viewed as a sample of the LRFD space and its material properties and external loads are not known in prior at any time of service life. To understand and evaluate the actual behavior of an engineering structure, real-time structural monitoring and modeling taking into account its practicality and cost restraint is necessary during an extreme event. In this study, adaptive multi-scale modeling of structures is conceptualized and exemplified with a four-story, two-bay steel building. A structure is divided into many groups, each having similar geometries and identical materials due to structural symmetry. For each group, the most critical structural member referred to as “master memberâ€ is modeled with fiber elements and the remaining members called “slave membersâ€ are with beam and plates elements. The material behavior of the master member can be introduced to the modeling of slave members in real time, based on the premise that the latter can be related to the former in terms of construction processes and the noise characteristics can be related to the structural damage under various external loads. Emphasis is placed on the development of an overall adaptive multi-scale modeling framework with load monitoring and resistance evaluation in real time. Towards this end, the master member is instrumented with an array of sensors for material property and structural behavior monitoring, and the slave members are numerically simulated with a finite element model established in ABAQUS. To verify and support the premise, finite element updating is performed in this study to ensure that the interface between the master member and the slave members is compatible in terms of forces and displacements under a predetermined evaluation criterion.
Model Updating of Aero-Elastic Spinning Finite Elements for SHM of HAWT Blades
As wind energy farms are built in more remote locations, autonomous operation of wind turbines is critical for the future of wind energy production, including a need for comprehensive conditional monitoring. Effective structural health monitoring of turbine blades is made difficult by their complex geometries, nonlinear behavior, stochastic loading, and spinning nature. To overcome these challenges, spinning finite elements have been developed fused with classical stochastic aero-elastic models using a random-vibrations approach to generate probability density functions for structural demand. Along with the distribution of the structural resistance of the turbine blade, these models yield the probability of failure of a blade under a given set of loading conditions. Prior studies have developed and validated the blade models. The present study investigates a modelupdating technique that can be used to characterize the structural demand for a given blade from acceleration data collected during operation. Simulated annealing is used as a search algorithm to update the modal properties and wind excitation parameters upon which the aero-elastic interaction model is based. Results from a 2.5 m turbine are presented.
Contribution of Fundamental Lamb Wave Modes to the Voltage Signal Output of a Piezoelectric Transducer
Structural Health Monitoring (SHM) systems are a very useful tool to know in real time the actual condition of a structure. Most of these systems are based on the generation, propagation, reception and interpretation of elastic waves in complex structures such as those used in aeronautics. An exemplary SHM system, like for example PAMELA SHM™ system has a wide range of potential applications, from structural integrity, impact detection, to stress, vibration or deformation distribution mappings. These specific applications have common difficulties associated, mainly related with the output signal analysis and interpretation. The signal analysis is a complex task due to dispersive and multimode characteristics of ultrasonic Lamb waves in elastic thin plate-like structures which makes the interpretation of inspection results extremely difficult. The main objective of the current work is to obtain a numerical model able to convert a displacement on the surface of a structure into a voltage signal received by the transducer and vice versa, as well as to quantify the contribution of the symmetric- S0 and antisymmetric-A0 zero order modes of a Lamb wave to the total voltage output obtained in a piezoelectric transducer. The proposed method is based on the conversion of the displacement history in a point on the surface of a structure into a voltage signal, by means of the piezoelectric constitutive equations that define the electro-mechanical coupling behavior. In the first place, a measurement of the displacement field is obtained by means of numerical simulation or experimental based methods. The voltage signal for a given point in the structure (for example in the position of a virtual transducer) is rebuild from those measurements and the electro-mechanical coupling relationship. Finally, the voltage signal is decomposed, based on the physical interpretation of the extensional Lamb wave mode S0 and the flexural mode A0, into the S0 and A0 mode contributions.
Analytical and Spectral Methods for Lamb Wave Simulations
In applications where Lamb waves based damage detection methods are considered, conventional linear and quadratic finite elements commonly used to model elastic waves are inefficient. The required mesh to obtain relative accurate results derives in enormous computational cost when simulating the wave propagation behavior in time domain. Analytical methods offer fast and accurate results, but their application are limited to relatively simple geometries. To solve this problematic a hybrid analytic- spectral-element approach is developed here. Drawbacks of both formulations can be circumvented if we model the regions belonging to the plate geometry with analytical methods while the perturbations of the plate-like geometry are modeled using high-order (spectral) elements. This paper presents an efficient method to do the coupling. Using this hybrid formulation the numerical effort is reduced drastically. The functionality of the proposed scheme is shown studying the propagation of ultrasonic guided waves excited and received with piezoelectric transducers.
Identification and Monitoring of Structural Parameters as Damage Indicators for Plates in the Post-Buckling Regime
Structural Health Control (SHC) is a holistic and comprehensive process that integrates structural analysis, structural health monitoring (SHM), damage assessment, damage prognosis and condition based maintenance (CBM). Main idea of this concept is to use the information of a structural analysis to identify structural parameters as damage indicators. By means of the structural parameters the sensor layout of the SHM system is designed and optimized, which is called Smart SHM. By means of numerical simulations the effect of damages on the post-buckling behavior of a thin plate under compressive loading and its failure modes is analyzed. Appropriate structural parameters are identified to display the post-buckling behavior and the damage effects. These structural parameters are monitored with strain gauges to diagnose damage. The corresponding sensor layout is designed and optimized to measure the structural parameters in a robust and reliable way. This procedure constitutes the Smart SHM system. The subsequent evaluation of the structural parameters, which are monitored during service, diagnoses the damage. The functionality of the Smart SHM system in the context of the SHC concept is verified in tests of a thin aluminum plate.
Finite Element Simulation of a Beam with Closed Crack and Breathing Crack Based on Subharmonic Detection Method
The occurrence of cracks in a structure which operate under loading conditions is a threat to structural security. Recent researches on damage detection commonly focused on breathing crack. However, most cracks in engineering structures are found to be closed crack. It is different from breathing crack that closed cracks is difficult to detect when they are closed for they can only be obviously observed when they are open. Subharmonics response is a competitive approach of detecting and evaluating closed crack but subharmonic responses may not always occur and are dependent on the input excitation conditions. The generation of subharmonic is under the effect of crack surface conditions, such as frictional force and the asperity. Two finite element models of a beam containing closed crack and breathing crack are developed, respectively. The model with closed crack make use of local node splitting and nonlinear spring elements at the crack surfaces, the modeling is based on the fact that the relationship of stress-strain is nonlinear at the crack region which varies with different crack surface conditions. And the model with breathing crack is established with contact pair which is treated as a frictional contact problem between the crack surfaces. Subharmonic in the model with closed crack will markedly increases when the conditions of excitation reaches a certain magnitude so-called threshold, while the model with breathing crack do not have such behavior at the same excitation. A comparison of the responses derived from two models using Fourier transforms for analysis is made to distinguish and investigate the conditions including frequency and amplitude on the specified excitation to the two models which will produce subharmonics.
Elastic Constants Identification for Laminated Composites Based on Lamb Waves Propagation
The paper proposes a method of estimating elastic constants of thin plates based on the guided waves propagation and the inverse problem solution. In the proposed approach a Scanning Laser Doppler Vibrometer measurements are employed to find the dispersion curves of Lamb waves propagating in thin plates. The virtual counterpart of the experiment is a numerical solution of high frequency wave propagation problem using the Local Interaction Simulation Approach. The experimental and simulated data are combined in a model-based material constants identification framework. The effectiveness of the proposed approach is demonstrated on an isotropic aluminium and composite plates. The results of the identification procedure are compared with the classical materials testing techniques in order to prove the good accuracy of the proposed approach.
Modeling of Lamb Waves in Composites Using a New Third-Order Plate Theory
The effect of shear deformation and rotatory inertia should be taken into account in modeling Lamb waves in composite laminates. However, the first and second-order shear deformation plate theories which do not satisfy the stresses free boundary condition are uneconomic approximations, because one has to develop a complicated scheme to compute the shear correction factors. The stresses free boundary condition requires an in-plane displacement field expanded at least as cubic functions of the thickness coordinate. Hence, in this paper, the dispersive curves of Lamb waves in laminates are calculated according to a new third-order shear deformation plate theory considering the boundary condition. The lower anti-symmetric Lamb modes results of the new theory are closest to the exact solutions from 3D elasticity theory compared to several existing plate theories.
A Vibro-Haptic Human-Machine Interface for Structural Health Monitoring
The goal of the structural health monitoring (SHM) community has been to endow physical systems with a nervous system not unlike those commonly found in living organisms. Typically the SHM community has attempted to do this by instrumenting structures with a variety of sensors, and then applying various signal processing and classification procedures to the data in order to detect the presence of damage, the location of damage, the severity of damage, and to estimate the remaining useful life of the structure. This procedure has had some success, but we are still a long ways from achieving the performance of the nervous systems found in biology. Primarily because contemporary classification algorithms do not have the performance required. In many cases expert judgment is superior to automated classification. This work introduces a new paradigm. Instead of trying to build a nervous system from scratch, we propose interfacing the human nervous system to the distributed sensor network located on the structure and developing new techniques to enable human-machine cooperation. Results from the field of sensor substitution suggest this should be possible. Sensor substitution is a process by which a human can partially regain the use of a lost sense using a different sense as a surrogate. The plasticity of the human brain allows the human to interpret the stimuli to the alternative sense as coming from the original sense that was lost. Recent advances in smart structures haptic technology have enabled a wide range of new human-machine interfaces to facilitate sensory substitution. Examples of these interfaces include force feedback for robotic applications, refreshable Braille displays, and prosthesis devices. Thus far, haptic devices have mainly been limited to physical applications. It is possible that haptic devices may allow humans a new interface to interact with abstract entities such as the data from a wireless sensor network, the topography of a high dimensional cost function, or the structural health of a wind farm. This study investigates vibro-haptic human-machine interfaces for SHM. The investigation was performed using a surrogate three-story structure. The structure features three nonlinearity-inducing bumpers to simulate damage. Accelerometers are placed on each floor to measure the response of the structure to a harmonic base excitation. The accelerometer measurements are preprocessed using Principal Component Analysis (PCA) via
Automated Near-Optimal Feature Extraction Using Genetic Programming with Application to Structural Health Monitoring Problems
Structural health monitoring systems at their core measure structural response and infer real-time damage and performance information. Conventional data processing involves extraction of low-dimensional features from time series measurements that are then input to a classification or outlier detection algorithm. Desirable features are highly sensitive to changes of interest in the structure while also robust in the presence of noise and varying operational and environmental conditions. Traditional feature design requires experts with domain-specific knowledge resulting in an expensive and time-consuming process. Recently, genetic programming, an evolutionary computation method, was adapted to provide automated, data-driven development of feature extraction processes with minimal user input in a supervised learning approach. This study experimentally validates the genetic programming approach with comparisons to common feature sets. Demonstrated applications include ultrasonic damage detection, condition monitoring for rotating machinery, and vibration-based structural health monitoring.
High-Speed, Non-Contact, Baseline-Free Imaging of Hidden Defects Using Scanning Laser Measurements of Steady-State Ultrasonic Vibration
We present an approach to visualize damage through rapid, full-field measurements of a plate-like structure’s steady-state response to a single-tone ultrasonic excitation. In our setup, a piezoelectric transducer provides the excitation while a 2D scanning laser Doppler vibrometer (LDV) makes the full-field response measurement. In taking measurements of a structure’s relatively high amplitude single-tone steady-state response, we enable significantly faster scans by avoiding many of the signal-to-noise ratio issues typically associated with LDV ultrasonic measurements. Damage is visualized by making automated estimates, on a pixel-bypixel basis, of the wavenumber of the excited response through wavenumber-domain processing. We provide sample imaging results for hidden corrosion spots in a metallic plate and delamination in two composite specimens.
Blind Identification of Structural Damage via Independent Component Analysis
The novel unsupervised blind source separation (BSS) technique is able to recover the underlying sources and their associated characteristic factors using only the measured mixture signals. This paper proposes to use the BSS learning algorithm— independent component analysis (ICA)—for blind identification of structural damage. Under the weak assumption of independence, ICA is derived to bias towards sparse components, which typically signify damage information. The measured structural responses are first transformed into the wavelet domain and then fed as mixtures into the BSS model. ICA is used to solve the BSS model, simultaneously yielding the “interestingâ€ source with sparse representation that indicates damage instant, as well as its associated spatial signature in the recovered mixing matrix revealing the damage location. Numerical simulations and laboratory experiments show that the proposed ICA algorithm performs accurate blind identification of structural damage, whether in single or multiple events. The proposed ICA damage identification algorithm is straightforward and efficient to implement. It is output-only and unsupervised, requiring no excitation information or any knowledge with respect to the structure. Besides, it is robust to measurement noise. The FastICA algorithm enjoys cubic convergence rate, which enhances the proposed method with computational efficiency for online monitoring of structures.
Design of Observer Based Crack Detection Filters and Their Application to Crack Detection and Isolation
In this paper the notion of a crack detection filter is introduced and algorithms for the design of such filters presented. The concept is based on the design of observers that sensitive or insensitive to the presence of a crack in a structure. It arises from an application of fault detection filters to structures with cracks and is a novel and new concept. The feasibility of designing crack detection filters for structural health monitoring applications is then demonstrated.
Embedded Modal Analysis Algorithms on the Smart Wireless Sensor Platform PEGASE
Operational modal analysis and vibration based damage detection of engineering structures have become important issues for Structural Health Monitoring (SHM) and maintenance operations, e.g. on transport infrastructure. Methods from control engineering have been adopted and converted for the application on civil structures. Approaches like subspace-based system identification combine excellent theoretical properties under the unknown excitation properties of a structure with practical usefulness. In this paper, the implementation of covariance-driven stochastic subspace identification (SSI) on the smart wireless sensor platform PEGASE is described. Special care is taken about the fast implementation of this technique since the computations are embedded on the platform and perform in real-time. The most efficient and current version of subspace algorithms has been implemented. Efficiency and memory consumption are primary criteria in this implementation. First validated results will be given for each step of the algorithms: crosscorrelation on natural inputs signal from sensors; Hankel matrix output; SSI implementation using the LAPACK library to get a SVD, pseudo-inverse, eigenvalues etc. Results validation has been correlated between PEGASE implementation and the previous processing in static situation: the same data was collected by wired sensors and data-loggers, then, later, processed on a PC using traditional Matlab software. In parallel, from an engineering point of view, a description of the PEGASE wireless platform will be given: generic usage, wide capacities, embedded Digital Signal Processing (DSP) processor and Library over a small embedded Linux Operating System, a very accurate synchronization principle based on a GPS/PPS principle, etc. Perspectives about a complete technical in-situ installation will also be given.
Application of Acoustic Emission Technique for Online Evaluation and Classification of Wear State
This paper introduces a low cost system for online detection, classification, and examination of wear phenomena in metallic structure based on the application of the acoustic emission (AE) technique and advanced signal processing approaches. First applications of suitable filtering techniques to define relevant data are explained. The main idea introduced here is to define and to monitor specific AE properties of a tribological system. Therefore experimental results, based on a test-rig for wear examination with variable lubrication and normal force are used. The measured AE signals are analyzed mainly by time-frequency analysis utilizing Xilinx-based FPGA board. The key idea is to connect signal features unambiguously to their unique sources, e.g. adhesive and abrasive wear, surface fatigue and so on to realize diagnosis (judging the system state) and prognosis (calculating State-of-Damage), and to monitor and control the effects. In addition to the method and filtering related aspects the measurements as well as the filtering hardware are explained and detailed results distinguishing different wear-related effects during different stages of the system’s life are given.
A Non-Dispersive Signal Construction Method for High Resolution Lamb Wave Damage Imaging
In practical applications, the spatial resolution of Lamb wave imaging is easily affected by the dispersion effect. In this paper, a non-dispersive signal construction (ND-SC) method is developed for dispersion compensation of Lamb waves. After the Lamb wave sensing model is established in frequency domain, ND-SC is theoretically studied. A high spatial resolution damage imaging method is developed based on NDSC and the traditional delay-and-sum algorithm. The proposed ND-SC and high resolution imaging methods are validated by experimental studies.
Sparse Signal Processing Technique in Multimodal Dispersive SHM
One of the main challenges in developing structural health monitoring algorithms is the multimodal and dispersive nature of the wave propagation. Structural health monitoring makes use of the wave propagation information in order to determine the distance between the measuring equipment and the defect from the reflections caused by the defect. The geometrical information from different sensor locations is then used to determine the spatial position of the defect. The inverse problem approach makes use of the knowledge of the whole mapping between the defect and the reflections. This mapping, also called forward operator, combines the wave propagation and the geometrical information in a natural way. Once the forward mapping is known, a regularization approach can be applied in order to reconstruct the defect out of the reflections in a stable way. The main aim of this paper is to find a formulation of the forward model for an isotropic multimodal dispersive medium, which contains all important features of wave propagation, but is simple enough in order to be used in a regularization approach. As we assume the defects to be few and small in size compared to the whole medium, our solution will be sparse in mathematical sense. In this case, sparse regularization approach is the most appropriate one to solve the inverse problem. We illustrate the application of the proposed method for defect detection by means of simulated Lamb waves.
Detection of Structural Damage Using the Exponential Sine Sweep Method
Structural damages can result in nonlinear dynamical responses.
Identification of Bending Stiffness Distribution in RC Plate Using Distributed Fiber Optics
Brillouin Optical Time Domain Reflectometry (BOTDR) is a promising candidate for static damage evaluation in large structures as it allows continuous distribution of strain along the structure. In previous works, it was found that even though the inherent limitation of spatial resolution of approximately 1m, a quantified parameter for very localized damage as cracks and an equivalent bending stiffness distribution can be found in RC beam. To use this concept in plate-like structures, estimation of the curvature distributions along x and y directions with respect to the capability of the BOTDR technology should be considered, as well as, consideration to the influence of the redistribution of the internal forces and moments on the curvature distributions. Therefore, the aim of this study is to investigate these issues in details and to suggest a tailored identification procedure for plate-like structures based on the BOTDR technology.
Temperature Compensation Based on Hilbert Transform and Instantaneous Phase for Lamb Waves-Based SHM Systems of Aircraft Structures
Variation of environmental conditions, e.g. temperature, influence wave propagation and therefore can create serious issues limiting application of guided waves (GW) in SHM systems. Temperature variations cause a change of wave velocity, which can be observed as a signal stretching. Therefore, although no damage occurs, the signal captured during the monitoring process, does not match the baseline signal. This may lead to frequent false alarms if the alarm level is low or to disregarding damage if the alarm level is too high. In this paper a novel method for compensation of the temperature influence on Lamb wave snapshots is presented. The first step of the technique relies on the calculation of signal’s instantaneous phase using Hilbert transform. Next, phase of the resulting complex analytic signal is shifted to match the phase of the baseline signal. Finally, the inverse Hilbert transform is applied to the result, which leads to aligned of the instantaneous phase of both waveforms.
Effect of Applied Load on Guided Wave Monitoring of a Composite Bolted Joint
A structural health monitoring study based on ultrasonic guided wave inspection is presented for the monitoring of a composite bolted joint under applied load. Bolt bearing failure in particular is selected as the type of damage studied. This damage mode is of interest for aerospace applications due to the increased prevalence of bolted connections and the complex mechanics required to predict the onset of damage in composite materials. The phenomenon is investigated experimentally by fabricating a carbon fiber reinforced polymer specimen and instrumenting it with macro-fiber composite sensor-actuators. The bolt fixture is designed to apply a tensile load to the specimen so as to introduce bolt bearing failure. The sensors are arranged in a circular array around the bolt hole to study the interaction of the guided waves with damage for different angles. Measurements are then made on the plate alone, with the plate in the bolt fixture, and finally with the specimen placed in the tensile testing machine at various levels of load. After collecting a sufficient amount of baseline data, more measurements are taken for a single tensile test where load is increased until failure occurs, pausing at three different levels of damage. Because bolt bearing damage changes the mechanical properties of the material locally, guided waves will scatter differently when the damage is present than they do for the undamaged plate. The objective of this work is to characterize the influence of applied load on the ultrasonic measurements made. First, the effect of loading on the recorded ultrasonic data is observed. Physical explanations for the load-dependent behavior are proposed. A technique for monitoring the load state of the specimen from the ultrasonic data is also presented. Because most structures are subjected to changing load states in service, it is anticipated that understanding the changes due to load state will be important for implementing effective guided wave structural health monitoring systems in the field.
Adaptive Regression for Damage Detection in Bridges Under Environmental Influence
Many of Vibration-based structural health monitoring (SHM) often uses vibration characteristics to detect damage. It has been acknowledged that changes in vibration characteristics can also happen due to environmental factors. Especially for civil structures it is impossible to test in isolation from natural environment. These environmental factors are seen to significantly affect modal frequencies and can easily mask changes caused by damage. In this study we propose a technique that treats temperature variations as embedded parameter and helps to define a correlation structure between modal frequencies. The proposed method utilizes the correlation between structural frequencies to represent the healthy state of the structure. Damage is detected by measuring deviation of the recorded frequency from the healthy state. The proposed damage detection strategy is tested using simulation result for a simply supported T-beam section bridges.
Structural Damage Detection Using Ultrasonic Guided Waves Under Varying Ambient Temperature and Loading Environments
Accurate interpretation of in-situ piezoelectric sensor signals is an enormously challenging task. This paper presents the development of environmental compensation models based on physical insight to aid in structural damage detection using in-situ piezo-sensor signals. Analytical as well as numerical simulation studies were carried out to understand the influence of varying ambient temperature and loading environments on signals collected from thin, metallic, platelike structures instrumented with surface mounted piezoelectric transducers. Numerical compensation models were developed and validated to estimate piezo-sensor response under known environmental conditions. A very limited amount of actual sensor measurements are required to estimate model parameters, making this approach to be efficient and practically useful.
Characterizing Variability in Strain Measurements Resulting from Temperature Changes Affecting Data Acquisition Equipment
This article reports the results of experiments conducted on the National Instruments Compact-RIO (cRIO) data acquisition equipment (DAQ) aimed at characterizing the contributions to strain measurements originating from the DAQ instrument, separate from those of the strain gauges and ultimately the structure being monitored. In most laboratory based measurement situations, the DAQ is at a fixed environmental temperature, and thus does not contribute to the measured strain. In SHM applications, however, this is not necessarily the case. To better understand the relationship between temperature and instrument induced strain, a series of environmental chamber tests were conducted. These tests measured strain from twelve metal-foil strain gauges bonded to an unloaded steel beam which was subjected to temperature variations in the range of –20°C to +20°C. Three separate test cases were explored. One with the cRIO at room temperature while a test beam and gauges were subjected to temperature variations. A second with the test beam at room temperature and the cRIO undergoing temperature variations. And a third with both the cRIO and test beam in the same variable temperature environment. These experiments clearly show that a significant contribution to apparent strain comes directly from the DAQ instrument itself. Strains reported by the cRIO when only the temperature of the test beam was changing indicate a difference of as much as 30με when compared to the case where the instrument and beam were both at the same temperature. Furthermore, the extent of the variation is different from input channel to input channel, and must therefore be characterized for each channel separately. Strains measured by the cRIO when only the instrument itself was undergoing a temperature change showed an approximately linear relationship.
Long-Term Monitoring of Modal Parameters for SHM at a 5 MW Offshore Wind Turbine
The analysis presented focuses on long term behavior of modal parameters of a 5 MW AREVA M5000 offshore wind turbine (WT). For the analysis, 30.000 data sets, with ten minutes length each, over a period of 17 months were investigated. Each data set holds 24 channels of 50 Hz acceleration data at six different levels and seven environmental & operational conditions (EOCs, 10 min mean). For the given data classification, the major operational states of the WT serve as orientation. Modal parameters are extracted on the basis of stabilization diagrams for different orders of data-driven stochastic subspace identification (SSI-data) in combination with a newly developed, triangulation-based extraction of modal parameters (TEMP) which allows an automated and fast analysis for all collected data sets. Linear trends of modal frequencies and damping are extracted, giving a broad overview of the structures dynamic behavior under varying operational states. Results yield important knowledge for future model updating, simulations and SHM-applications.
Influence of External Excitation on Electromechanical Impedance Signatures
The using of electromechanical impedance (EMI) technique have been increasingly leading piezoelectric-ceramic (PZT) transducers to be applied to structural health monitoring (SHM) of real structures such as bridges, buildings, and airplanes. The PZT transducers are usually bonded on the structural surface and subjected to actuation so as to detect the damages in structures at the desired frequency range. The interrogation results in the electromechanical impedance signatures that will be changed by the presence of damage in structures which can be used to estimate the structural health or integrity according to the changes of the signatures. However, in fact most of the structures are in the vibration environment, and the effects of vibration on EMI signature may lead to erroneous diagnostic results of the structural integrity. So far there have yet been few investigations which take into account the effects on impedance signature. In order to obtain reasonable results, the effects of external vibration need to be considered when the electromechanical impedance technique is used to detect the damage of the structure. In this paper, three-dimensional finite element simulation of a simply supported plate with one PZT transducer bonded on surface is carried out to study the effect of external vibration on the PZT admittance signature. During the process of PZT scanning frequency, different frequencies of external vibration are imposed on the plate. Furthermore, experiments using a beam specimen mounted on a shaker which generate the external excitation are also conducted to demonstrate the effects. The results of the numerical simulation and the experiment demonstrate that different frequencies of the external excitation produce different degrees of effects on signatures.
Accurate Temperature Compensation and Characterization of Overall System Thermal Response Using Total Measured Strain in FBG Sensors
One of the most common fiber optic sensor (FOS) types used are fiber Bragg gratings (FBG) -based strain sensors are simultaneously sensitive to both temperature and strain. Thus it is essential to utilize sensors that are either fully temperature insensitive or, alternatively, properly temperature compensated to avoid erroneous measurements. In this paper, we introduce the concept of measured “total strainâ€, which is inherent and unique to optical strain sensors. We review and analyze the temperature and strain sensitivities of FBG strain sensors and decompose the total measured strain into thermal and non-thermal components. We explore the differences between substrate CTE and System Thermal Response Coefficients, which govern the type and quality of thermal strain decomposition analysis.
Critical Issues Affecting the Performance of PWAS Based SHM Systems for Corrosion Detection of Aluminum Structures
Structural health monitoring (SHM) techniques for early detection of corrosion damage include guided waves techniques based on the use of piezoelectric wafer active sensors (PWAS) bonded to the structure to be monitored. A reliable SHM system using PWAS is affected by several issues, including the type of the adhesive used to bond the PWAS to the substrate, the environment on the signals measured and the introduction of controlled defects to simulate corrosion damage. This paper investigates the above mentioned issues applied to a PWAS based SHM system for corrosion detection in aluminum structures. Two types of adhesives are investigated, namely epoxy and cyanoacrylate resins. They are compared in regard to long term and thermal properties. The difference in regard to their performance is traced to the degree of cure of the resins and to their glass transition temperature. Furthermore, electrochemical etching is introduced as an efficient tool to simulate corrosion damage in aluminum.
A Notional Framework and Model to Improve Monitoring of Structural Health Systems
Structural health monitoring (SHM) systems have rapidly advanced, embracing both technological advances and real-time, in situ monitoring. Such advances are paramount to maintaining structural integrity. Capitalizing on continual data collection, however, SHM systems can reach beyond detection of structural damage to include the development and prediction of time to critical failure. Due to this increased capability, it is inherent that SHM systems capture information differently than current inspection methods. We advocate the increased capability of the SHM system as a difference between assessing a structure and monitoring a structure. This work introduces a framework with which the data captured in the monitoring of structural health is leveraged in order to improve not only the prediction of the state of the structure (presence of damage and extent of damage), but also the estimated time to failure. A statistical model is presented to represent the generalized framework. This model estimates the time to critical crack length in a hierarchal model such that the time until critical crack length occurs is modeled as a function of current crack status, that is, crack length and location. Crack length and location are themselves a joint response modeled from available sensor output and potentially other various environmental or sensor-related measurements. The necessary joint modeling of length (horizontal and vertical) and location allows us to incorporate the inherent correlation between these as part of the estimation process. These estimates are then used to predict the time until failure (critical crack length is achieved). By modeling in this hierarchal fashion, the current and past state of the structure, as well as changes in the structure during the course of monitoring, may be used to more precisely estimate the remaining life, that is, the time until structural failure may occur. Simulated and preliminary data from current specimens will be used to demonstrate these methods.
Non-Destructive Detection of Crack Initiation Using Acoustic Emission
One of the major concerns in structures is early detection of a growing crack to prevent fracture, predict remaining useful life, schedule maintenance and reduce costly downtimes. This paper focuses on in-situ monitoring of structural health, specifically detection of small crack growth and crack initiation in structures using Acoustic Emission (AE) technology. Acoustic Emission (AE) is a non-destructive testing (NDT) with potential applications for locating and monitoring fatigue cracks during structural health management. In this paper, the AE signal properties for identifying the presence of a small initial crack is assessed to provide the presence of the onset of a potential growing crack. The approach is based on establishing any association between particular features of AE and fatigue crack initiation. Experimental investigation of uniform cyclic load tests performed on standard fatigue samples of aluminum alloys showed that certain properties of the AE events noticeably change after crack initiation. It was concluded that AE technology can successfully detects crack initiation. The proposed method has significant potential to be used for in-situ monitoring and evaluation of health of structures.
Bayesian Updating of Detection Capability with Frequency Response Function Related Structural Health Monitoring Features
In all structural health monitoring (SHM) applications, feature extraction plays an important role, as it determines the most sensitive and specific metrics on which to base decision-making. . The Frequency response function (FRF) is a widely-used category of features because of its clear physical interpretation. Often, FRF is estimated from the acquired structural excitation and response, and the data obtained are always subject to uncertainty. As a result, this uncertainty typically propagates and compromises the estimations of the FRF, which degrades its performance in decisionmaking problems. With uncertainty quantification (UQ) models, the damage diagnosis problem is converted into a statistical significance detection procedure. Bayesian statistics collects evidence to make decisions and is very powerful for model and model class selection. For instance, the aforementioned transformed damage detection problem may be further implemented as a recursive model selection process in between various distribution models (usually binary cases such as undamaged and damaged conditions). Instead of evaluating the total likelihood of FRF observations, this paper adopts Bayesian framework to update the posterior probability of trinary model selection, given increasing number of measurements. Testing data are obtained from the Machinery Fault Simulator (MFS) from SpectraQuest, in which two damage scenarios, namely damaged ball bearing and damaged outer race, are included. Posterior probability for each damage condition outperforms traditional likelihood evaluation, and this recursive implementation distinguishes both damaged conditions in this paper, and works for both FRF magnitude and phase.
Modular Signal-Based Condition Monitoring of a Hydraulic Servo-System
This paper investigates the use of the standard available measured signals in a hydraulic system (the pressure signals of the two hydraulic cylinder chambers) to develop a modular signal-based monitoring system. The developed system has to detect and classify the typical faults that can occur in the hydraulic systems, e.g. internal leakage, dynamic friction, or undissolved air in the hydraulic cylinder chambers. The proposed monitoring system consists of different detection modules as well as a decision fusion module. Short Time Fourier Transform (STFT) and other statistical feature extraction tools, such as arithmetic mean value, are realized to extract the relevant features of the pressure signals. The extracted features undergo a further filtration process to classify the possible occurred faults. A decisionfusion module is used to provide a final decision about the system state as well as to identify the type of the possible occurred fault. The developed approach is able to detect the state of the hydraulic system under all considered operational conditions. Furthermore it is able to identify the type of most of the possible occurred faults.
Fatigue Damage Prognosis in FRP Composites by Combining Multi-Scale Degradation Fault Modes in an Uncertainty Bayesian Framework
In this work, a framework for the estimation of the fatigue damage propagation in CFRP composites is proposed. Macro-scale phenomena such as stiffness and strength degradation are predicted by connecting micro-scale and macro-scale damage models in a Bayesian filtering framework that also allows incorporating uncertainties in the prediction. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in CRFP cross-ply laminates. Results are presented for the prediction of expected end of life for a given panel with the associated uncertainty estimates.
Triboluminescent Optical Nerves for Smart Concrete Structures
In-situ and real time structural health monitoring is critical for ensuring the safety of users and for extending the useful life of our aged and overloaded civil infrastructure systems like concrete bridges and dams. The development of the insitu triboluminescent optical fiber (ITOF) sensor may provide concrete structures with these capabilities. Two types of cement-based composites with integrated ITOF sensors were fabricated and tested under flexural loading. Real time failure detection was demonstrated in mortar beams while real time damage monitoring was demonstrated in reinforced concrete (RC) beams. For all samples tested, significant and distinctive jump in the triboluminescent (TL) signal values were detected at the instance of failure in mortar beams and cracking in RC beams. Analysis of the TL signal profiles from the brittle failure of the mortar beams revealed multiple peaks indicative of multiple TL emissions that provided evidence of the sensor’s ability to sense rapidly propagating crack during mortar failure. TL signals were observed at the instance of crack formation (damage) in the tested RC beams that provided early warning of system’s degradation before failure.
Beamforming in Plates Using Embedded Acoustic Metamaterial Lenses
In this paper we present a novel technique to design a single ultrasonic transducer system for selective damage interrogation. The design is based on the concept of acoustic metamaterials. The structural area around the transducer is dynamically tailored by using a periodic distribution of resonant inclusions acting as an acoustic lens. The inclusions are arranged in order to form a circular anisotropic lens having an ultrasonic transducer located at its centre. The properties of the lens can be locally tailored in the azimuthal direction allowing controlling the direction of excitation by simply tuning the frequencies of excitation. Numerical results show that the proposed lens design is able to create highly directional and collimated ultrasonic beams by using a single transducer, therefore, overcoming some of the major limitations due to phased array technology.
New Proposal for Improvements in Communication and Power Systems for SHM Aircraft Networks
There are many communication and network systems that have been developed and implemented by the aeronautic manufacturers during the last decades to interconnect the many and very different sensors included in the aircraft. In the Structural Health Monitoring (SHM) field, the quantity of SHM sensors to be installed inside an aircraft is of the order of hundreds or thousands, which complicates even more the complexity of the communication networks. The high consumption of all the SHM systems working at the same time and the complexity of the complete SHM network are critical aspects, besides the size and weight of the wiring, the periodicity of the sampling of these sensors, the data rate or the real-time requirements. Previous work of the authors presented an all-in-one fully integrated on-board electronic system that can perform in-situ SHM of aircraft’s structures based on guided wave ultrasonic method. The consumption of this equipment is of a few Watts, which is a critical specification that must be resolved if this system is to be boarded in the aircraft. The goal of the research presented in this paper is design a communication system that can interconnect a huge number of SHM devices installed on-board an aircraft with a low consumption. The distribution of these devices has been organized in sub-networks in order to reduce the instantaneous consumption. Multipoint communications and a new protocol have been adopted. A prototype to test this network system has been designed, developed and subjected to laboratory tests.
Energy Efficient Wireless Sensor Network Architecture for Aircraft Structure Health Monitoring: From Sensor to Data Collect
Aerospace testing and monitoring systems are based nowadays on point-to-point wiring, which results in heavy cables, difficult installation and maintenance and a limited number of sensors. Switching to wireless communication will allow to respond to these constraints but will create new challenges due to transfer reliability requirements, high data rate and energy constraints. In this context, there is a strong need for energy efficient wireless sensor communications as each node is powered from a battery or from energy harvesting. This paper describes a wireless communications architecture tailored for this purpose, based on a low power Impulse Radio Ultra Wide Band (IR-UWB) physical layer and an energy efficient TDMA MAC layer. The whole measurement system for aircraft Structure Health Monitoring was implemented and tested on a G500 aircraft mock-up with good results. The complete system architecture from sensors to data collect is presented.
Structural Condition Monitoring of Helicopter Components
This paper will review the ongoing Structural Health Monitoring (SHM) work being done for the Aviation & Missile Research, Development, & Engineering Center (AMRDEC) Diagnostics and Prognostics Laboratory (DPL) located at the Redstone Arsenal in Huntsville, AL. The focus of DPL activities is to examine various techniques to enhance the diagnostic capability of Army aviation, ground, and missile systems. An area the DPL is involved in is structural health monitoring of critical structures of our aviation fleet. This work involves detecting and, in some cases, localizing damage on various air and rotor craft parts. Many of these applications are currently displayed as demonstrations in the DPL. Two laboratory demonstrations were developed to demonstrate the importance of optimizing sensor locations by comparing the results of a group of sensors that have been optimized to a group of twice as many sensors that had been heuristically placed. This is demonstrated on two very different parts; a Black Hawk drag beam tested in free-free boundary conditions and a Kiowa Warrior roof strap tested with many complex boundary conditions. The drag beam demonstration detects a simulated mass removal such as pitting or corrosion where as the roof strap demonstration detects simulated cracking and bolt loosening. Another demonstration involves detecting damage such as pitting and corrosion on an Apache tail rotor blade. Although the demonstration uses external sensors and actuators, the end goal would be to have the sensors and actuators integrated into the part during manufacturing. The correlation between the damage metric and corrosion are demonstrated on steel plates subjected to a salt fog chamber.
Decentralized Health Monitoring for Beam-Like Truss Structure Using Wireless Acceleration Lab Measurements
This paper presented a decentralized health monitoring study for beam-like truss structures with a lab verification using wireless acceleration measurements. According to structural geometrical layout, the structure is divided into multiple local subgroups firstly. Based on vibration test within each subgroup, structure dynamic flexibility matrix of each measured subgroup is estimated and structural damage locating vector is computed. Structural damage location can then be detected among each subgroup. The final decisions on structure damage occurrence and location are made by comparing the results obtained from different subgroups. Both numerical and experimental studies on a cantilever beam-like truss structure are conducted to demo the procedure. The results verify that this model-dependent health monitoring method is applicable to detect and locate elemental stiffness reduction in the truss structures even when some modeling errors exist.
Wireless Sensing Systems for Rotorcraft SHM
The goal of this work was to demonstrate, in an operational environment, a network of hard-wired and wireless sensors for next generation rotorcraft structural health and usage management systems (SHUMS). Wireless sensors on the rotating components included energy harvesting to eliminate battery maintenance. The SHUMS system included a wireless sensor data aggregator (WSDA), an energy harvesting wireless pitch link node, a wireless structural vibration sensor node, wireless structural strain sensor nodes, and a hard wired attitude heading reference sensor (AHRS). With the Global Positioning Systems (GPS) as a timing reference, the WSDA used periodic wireless beaconing to maintain precise time synchronization among the all nodes. The system was installed on a Sikorsky MH-60S. Synchronized wireless and wired sensor data, collected at various sample rates, were successfully aggregated using time as a unifying variable. This system can monitor a wide variety of machines and structures, including wind turbines, heavy equipment, rotating machinery, and helicopters.
Flight Demonstration of a SHM System on an OH-58 Aircraft
Acellent Technologies Inc. is working with the U.S. Army and Bell Helicopter to test a Structural Health Monitoring (SHM) system called the Tail Boom Crack Detection System (TBCDS) for the OH-58 aircraft. Sensors placed in the tail boom area of interest monitor the structure and communicate with the HON-1134 Honeywell Health and Usage Monitoring System (HUMS) and Personal Computer – Ground Based System (PC-GBS). The PC-GBS processes the data and displays the health of the monitored portion of the tail boom to the user. The TBCDS consists of SMART Layer sensors, an onboard ScanGenie II diagnostic unit, and SmartPatch software. The SMART Layer sensor is a network of piezoelectric (PZT) discs embedded on a thin dielectric film. Each disc can be used as an actuator or sensor, enabling active and passive real-time monitoring. The ScanGenie diagnostic hardware operates the SMART Layer and interfaces with the onboard HUMS system. The SmartPatch software, which typically resides on the ground station, analyzes the collected data to assess the structural integrity of the monitored component or area. This paper provides an in-depth view of the installation and flight-testing of the TBCDS system on the Bell OH-58 demonstrator aircraft. This includes the installation of the sensors pre-configured and designed for the tailboom area, wiring, brackets and mounting for the hardware and the tie-in into the OH-58 HUMS system. Data collected in different aircraft flight scenarios will be presented along with an in-depth analysis of the data.
An SHM System for Detecting Corrosion Damage in Aging Aircraft
Safety, reliability, and sustainment costs are of great concern for aging aircraft. With the many aircraft within the Air Force becoming older there is a need to inspect for corrosion in metallic aircraft structures. A major drawback of conventional NDI methods of inspection is that access to at least one side of the structure is required. This can require that the aircraft be removed from service and the structure disassembled for inspection, which can lead to a host of additional problems. Recent advances in structural health monitoring (SHM) have enabled new developments to enhance current NDI methods to facilitate the inspection of structural components that are difficult to access. Acellent® Technologies has developed SHM systems utilizing permanently installed sensor layers, off-board data acquisition hardware, and software for data analysis and 3D display of diagnostic results. This paper discusses the results of two Phase II SBIR projects performed by Acellent for the USAF to develop a method to inspect for corrosion damage in areas that are difficult to access without disassembling aircraft structure. The two projects were focused on detecting stress corrosion cracking (SCC) on KC-135 structure and pitting corrosion damage in multilayered structural components, such as the XW155.024 chord of the F-15. This paper discusses the challenges involved in the development and transition of this technology including issues concerned with installation of the sensors and design and testing of a new portable handheld device for data acquisition and analysis. Results of the validation testing conducted with the system on simple and complex components will be discussed along with practical issues concerning the implementation and use of the system on aircraft structures.
Prediction of Landing Gear Loads from Flight Test Data Using Gaussian Process Regression
The paper explores the use of Gaussian Process (GP) Regression for predicting landing gear loads from recorded in-flight parameters. Gaussian process regression is a powerful Bayesian machine learning tool whereby predictions and their distributions can be obtained without having to specify a particular model/functional form. Advantages of employing Gaussian Process regression in this case include its training speed in comparison to more traditional machine learning tools such as neural networks, and the ready availability of confidence intervals on predictions. The paper demonstrates the prediction capability of GP regressors applied to flight test data.
Structural Airframe Maintenance Strategy Comparison: A New Approach
Currently, structural airframe maintenance is based on preventive maintenance such as damage tolerance approach, which consists in scheduling inspections over the entire life of the aircraft. Damages that could in a near future threaten the in- tegrity of structures are detected and repaired at predetermined inspections. Signifi- cant researches made to improve structural maintenance costs led to condition-based maintenance using an embedded system such as a structural health monitoring system that continuously tracks the structure and gives a real-time health status. Our paper proposes a general mathematical framework based on a probabilistic model to com- pare the lifecycle cost of different structural maintenance strategies. Useful analytical formulas allow to quantify the average number of fuselage panels to be replaced af- ter any number of cycles and over the aircraft lifetime. The cost-effectiveness of condition-based maintenance alternatives using structural health monitoring systems can therefore be quantified and compared to traditional maintenances.
Damage Sensitive Features of a Repaired Helicopter Tail
A helicopter tail, with a fatigue induced crack near the rear rotor gear, was repaired by a bonded composite patch that was instrumented with Fiber-Bragg- Grating (FBG) sensing net. A repair damage detection algorithm, based on the Principal Component Analysis, was used for tracking anomalies in the vibration signature as recorded by the sensors. Sensors' readings, taken during engine runs, demonstrated excellent mutual correlations, indicating that the repair still maintains its airworthiness capability after more than four years of service. Using this airworthiness assessment concept, the many advantages of bonded repairs (smoother load transfer, good fatigue and damage-tolerance behavior of the composite patch, ease of application on curved surfaces) are preserved without the need for complex inspections techniques, thereby minimizing maintenance costs and A/C down time.
Optimizing Mode Tuning of Guided Waves and PWAS Electromechanical Impedance for Power Efficient SHM Systems
This study presents the use of guided Lamb wave mode tuning and electromechanical impedance (E/M) method with piezoelectric wafer active sensors (PWAS) for designing power efficient structural health monitoring (SHM) systems. The paper begins with a review of previous work on analytical modeling for power and energy of excited Lamb waves by PWAS into the structure. The study is achieved in three major parts. In the first part, Predictive modeling of Lamb wave tuning was performed analytically; then compared with measurements of both laser vibrometer and pitch catch received voltage signals. In the second part, the E/M impedance of PWAS bonded to a structure was evaluated analytically, by finite element modeling, and experimentally. In the third part, the paper describes how the E/M admittance and mode tuning were combined into the developed power model. Analytical model for power partitioning among multimodal Lamb waves was presented for 10 volts input excitation of a PWAS bonded to "- thick steel beam. Finally, a parametric study for wave power and energy was constructed for different transducer sizes and excitation frequencies. The paper ends with a summary, conclusions and recommendations for future work.
Effect of Bonding Layer on Admittances of PWASs Bonded on Thin Slender Substrates: Correlation Between Measurements and Simulation
The effects of the bonding layers on the admittances of piezoelectric wafer active sensors (PWASs) bonded on thin slender substrates are investigated in this paper. Experimental measurements demonstrated that the bonding layer strongly influences the resonant characteristics of the PWAS. Based on the time-of-flight of the wave packets at or near the resonance frequencies, we identified that the some resonances are due to the flexural mode of the substrate while the others are due to the extensional mode. To understand these effects, multi-mode analytical models were developed to simulate the admittances of the PWAS under these two vibration modes. The deformation of the PWAS was coupled to the substrate using a shear lag parameter. The parameters of the bonding were adjusted to match the simulated and measured admittances. A frequency dependent PWAS loss parameter is suggested for improving the match of the resonant amplitudes.
Analytical Modeling of Proof-Mass Piezoelectric Wafer Active Sensor for Symmetric Lamb Waves Tuning
This paper presents theoretical work on proof-mass piezoelectric actuator for tuning the symmetric modes of Lamb wave. In the first part of this study, a simplified three bar piezo-resonator was modeled using the resonator theory. Three bar resonator model includes a piezoelectric wafer active sensor (PWAS) in the center and two isotropic elastic bars bonded on both sides of the PWAS. The following assumptions were made for the three-bar piezo-resonator model. First, the geometry and the cross-section area of all three bars were the same although they have different materials and different lengths. Second, the two isotropic bars were assumed to be perfectly bonded to the PWAS on the interfaces. The three bar piezoresonator model was used to obtain the resonance frequencies for the normal mode expansion method. Essentially, this model was to build the basis for the proof-mass PWAS (PM-PWAS). The study was followed by proof-mass analysis to investigate desired control objectives (such as tuning of axial wave modes) using the correlation between a proof-mass transducer and structural dynamic properties in the substrate structure. The PM-PWAS transducer is configured with a PWAS actuator and proof-mass on top. Proof masses shift system resonance towards optimal frequency point. PM-PWAS transducer attached to an isotropic elastic plate was analytically modeled. A parametric study to indicate effect of the proof-mass size change on mode shapes in relation with frequency response function amplitudes at resonance frequencies. The paper ends with summary, conclusions, and suggestions for future work.
Integration of Health Monitoring and Control of Building Structures Due to Earthquake
A hybrid real-time structural health monitoring and control system for building structures is presented in this study. The first objective of the hybrid system is to detect the damage occurrence, location, and severity through an proposed interstory based acceleration feedback method. The second objective of the hybrid system is to maintain the desired response defined from the pre-designed healthy reference model contained in the health monitors through the use of an actuator in the damaged region. A model-reference adaptive control algorithm for the decoupled substructures was then developed and integrated with the interstory drift based acceleration feedback method for health monitoring. Little or no prior information about current health condition of the structure is required by the proposed adaptive control algorithm. The local adaptive feedback control actuates the actual substructures to track the desired response signals. Result obtained by numerical simulations for the illustrative example in this study shows the detection of the damages, the asymptotically tracking of the state of the substructure, and convergence of the time-varying parameters in the adaptive controller.
FEM Model Calibration of Sports Arena Stand, Using Monitoring Data
The paper describes a stepwise procedure to dovetail Finite Element Method (FEM) model data with data of monitoring the Ice Sports Arena vibrations in the city of Omsk (Russia). This procedure consists of 4 steps: data domain selection, modal parameters identification, objective function selection and optimization procedure. Natural frequencies of the structure (frequency-domain) were chosen to calibrate FEM model for one section of arena stand (further “Standâ€). Frequencydomain was selected because natural frequencies were used to assess structural health of the stand and the FEM model of this structure should have the same data format as the result. More adequate mathematical model of Stand was obtained as a result of FEM model calibration procedure.
Scenario Based Approach to Structural Damage Detection and its Value in a Risk and Reliability Perspective
A scenario- and vibration based structural damage detection method is demonstrated though simulation. The method is Finite Element (FE) based. The value of the monitoring is calculated using structural reliability theory. A high cycle fatigue crack propagation model is assumed as the damage mechanism. The statistical properties of the monitoring system (the distribution of the detectable damage) are estimated using a log-linear regression for signal response data from simulation. The value of monitoring is then calculated as the difference in expected utility for the structure with- and without monitoring. Monte Carlo Sampling (MCS) is used to estimate the probabilities and the tower of an onshore NREL 5MW wind turbine is given as a calculation case.
Leakage Detection in Water Pipe Networks Using Electromechanical Impedance (EMI) Based Technique
The electromechanical impedance (EMI) technique has been successfully used in structural health monitoring (SHM) systems on a wide variety of structures. The basic concept of this technique is to monitor the structural integrity by exciting and sensing a piezoelectric transducer, usually a lead zirconate titanate (PZT) wafer bonded to the structure to be monitored and excited in a suitable frequency range. Because of the piezoelectric effect, there is a relationship between the mechanical impedance of the host structure, which is directly related to its integrity, and the electrical impedance of the PZT transducer, obtained by a ratio between the excitation and the sensing signals. This work presents a study on damage (leaks) detection using EMI based method. Tests were carried out in a rig water system built in a Hydraulic Laboratory for different leaks conditions in a metallic pipeline. Also, it was evaluated the influence of the PZT position bonded to the pipeline. The results show that leaks can effectively be detected using common metrics for damage detection such as RMSD and CCDM. Further, it was observed that the position of the PZT bonded to the pipes is an important variable and has to be controlled.
Electrospun Nanofiber Based Strain Sensors for Structural Health Monitoring
A novel piezoresistive strain sensor technology is demonstrated based on scalable electrospinning of nanofibers (NFs), which is promising for low cost and large area structural health monitoring (SHM). Meshes of metallized core-shell NFs are used for fabrication of thin conductive textiles that demonstrate a high strain gauge factor in excess of 60, due to a disjointing/jointing mechanism of the inter-NF junctions that leads to changes in current paths and the overall resistance of the mesh. Cyclic, dynamic and temperature-dependence tests demonstrate stable and fast response to strain and the potential for this technology for large area, low cost, stable and low voltage strain sensing that helps in reducing cost and complexity of today’s real-time SHM of composite structures.
Screen Printed Piezoceramic Actuators/Sensors Microfabricated on Organic Films and Stretchable Networks
Integration, deployment, and installation of SHM network hardware remains a significant challenge inhibiting fielding of SHM systems. SHM systems typically consist of sparse arrays of numerous transducers and large scale wiring in order to span target structures. These systems are currently assembled, one piece at a time, and installed by hand leading to significant labor costs. Because of the size and weight of the components they adversely affect and are parasitic to the host structure. This is particularly detrimental to high performance aerospace structures. Microfabricated stretchable sensor networks have received a lot of interest recently and have the potential to overcome these issues. These systems leverage nonstandard C-MOS processing techniques to mass fabricate micro and nano-scale device in a single integrated system. The complete system of devices with interconnecting wires is then expanded to span an area orders of magnitude greater than the original fabrication area. The result is a complete integrated network of numerous of small scale devices that will have minimal parasitic effects on a host structure and can be installed monolithically over large areas. However, fabrication limitations previously restricted the types of systems that could be created on these networks to passive, resistive devices like resistive temperature sensors and wiring. This paper presents an overview of recent research that has enabled the mass fabrication and deployment of screen printed piezoceramic transducers on organic stretchable network substrates including fabrication, deployment, and testing. Ultrasonic signals typical to SHM were actuated and detected as were strain waves from impacts. Results indicate that the screen printed piezoceramic transducers released and deployed on an organic substrate produce similar signal amplitudes as a baseline sample as printed on a silicon substrate. This work enables the mass fabrication and deployment of large arrays of microfabricated sensors, applicable to ultrasonic damage detection systems, on stretchable networks. This has the potential to revolutionize structural health monitoring by simplifying fabrication, installation and reducing the parasitic effects on host structures like added weight or volume.
A Four-Channel, ±36 V Piezo Driver Chip for a Densely Integrated SHM System
Interface electronics represent the middle layer in the structural health monitoring (SHM) system architecture, bridging the physical sensors and the computational algorithms. In order to push SHM systems from the lab into practical deployments, it is necessary to construct this layer with application-specific silicon chips to achieve a high degree of integration. This paper describes a four-channel piezo driver chip for ultrasonic SHM, which is our first contribution towards the above goal. We have successfully fabricated the chip, validated its functionality and electrical performance, and demonstrated its usage within an SHM test bed. The paper further discusses plans to improve and enhance the chip, and proposes constructing a densely integrated SHM system by combining the chip with piezo elements on a flexible substrate.
Exploration of Ultrasonic Guided Wave Detection with Optical Fiber Sensors and Piezoelectric Transducers
This paper presents a comparative study of ultrasonic guided wave detection with optical fiber sensors and piezoelectric transducers. In recent years, fiber Bragg gratings (FBG) have been investigated by several researchers as an alternative to piezoelectric sensors for the detection of ultrasonic waves. FBG have the advantage of being durable, lightweight, and easily embeddable into composite structures as well as being immune to electromagnetic interference and optically multiplexed. However, there is no turn-key commercially available product that uses this promising technology for the detection of ultrasonic guided waves because: (a) the frequency is high (hundreds of kHz and above); (b) the strains are very small (less than one microstrain); (c) the operational loads may also induce very large quasistatic strains (the detection of very small ultrasonic strains superposed on large quasi-static strain presents a quite challenge). Although no turn-key optical system exists for ultrasonic guided wave detection, two commercially available FBG interrogation systems with various frequency ranges and strain resolutions (Micron Optics SM690 and Redondo Optics M200) are available and have been evaluated by us. This paper starts with a literature review of the state of the art in optical technologies for ultrasonic wave detection. Subsequently, this paper describes our comparative tests with several fiber optics systems. The measurement resolution and sampling speed were considered as the most important criteria in our test. In the final part, a commercially available tunable laser source from LUNA was customized for ultrasonic sensing. Parallel strain measurements were done with different systems including FBG and piezoelectric wafer active sensors (PWAS). The measurements were compared. Possible calibration and performance improvements for the optical interrogation system are also developed and discussed. The paper ends with conclusions and suggestions for further work.
Embedded Data Processing in Wireless Sensor Networks for Structural Health Monitoring
Eight wireless accelerometer sensor nodes (Imote2) equipped with energy harvesting solar panels were deployed continuously on an operational pedestrian footbridge in Singapore for two weeks. Each node periodically processed vibration data using a novel embedded data processing algorithm, referred to as the Filtered Hilbert-Huang transform, which resulted in a data reduction of 96%. From the processed results which the nodes transmitted to the base station, it was possible to conclude that resonant response from pedestrian walking excitation led to increased vibration levels during peak usage times. The maximum recorded peak and RMS acceleration were 52mg and 35mg respectively, which are within the limits allowed by several major design guidelines. This wireless sensor network deployment demonstrated the potential of decentralised, embedded data processing for wireless medium- and long-term structural health monitoring of civil infrastructure.
Intelligent Material Actuators and Their Applications for Structural Dynamic Control
Recently various intelligent materials have become the subject of scientific interest all around the world. The unusual properties of intelligent materials result in their numerous applications, but most of all their controllable properties make them scientifically very attractive. For those reasons intelligent materials such like: piezoelectric materials, electro- and magnetorheological fluids, thermally and magnetically activated shape memory alloys, for many years now have stayed in the focus of many researchers and have remained of their high interest. The exceptional physical properties of the intelligent materials allow them to be integrated within other materials in order to obtain structural characteristics that are uncommon in nature. In such a manner new types of materials of controllable and adjustable properties can be produced. Following that they can be integrated within various engineering structures or their elements in order to enable one to control static, dynamic or damping characteristics. The aim of this paper is to explore the applicability of the intelligent materials in that control process. This was achieved and demonstrated by the Authors based on a simple case study, in which a layered cantilever composite beam was considered as the object of investigation. Next, the application of various intelligent material actuators in the form of magnetorheological fluid and thermally or magnetically activated shape memory alloys were taken into account. Their influence on selected dynamic characteristics of the beam was carefully investigated. Necessary numerical calculations were performed by the Authors by the use of the finite element method and their own numerical codes written in MATLAB. The results of these calculations are presented in this work.
Multiple PAMELA SHMTM System for Automated SHM Control During Fatigue Tests of Aircraft Structures
Fatigue testing of aircraft structures is a complex, costly and time-consuming process. Typical primary-component fatigue tests of aircraft structures usually last between one and two years. In addition, this process must be interrupted periodically to check the structure integrity, usually using nondestructive methods, which increases the total duration of the process. The goal of the system described in this paper is to provide a solution for automating structural integrity inspection with structural health monitoring (SHM) methods. This solution would avoid periodic interruptions and produce cost-effective and timely test processes. This paper details the main features of and possibilities for the multiple PAMELA (Phased Array Monitoring for Enhanced Life Assessment) SHM™ system when it is used to automate SHM control during fatigue tests of aircraft structures. A complete software environment is described for automating test execution using the PAMELA III hardware.
Optical Fiber Sensor for Corrosion Detection and Evolution Monitoring in Reinforced Concrete Structures
Corrosion of reinforced bars (rebars) in concrete structures is a major issue in civil engineering structures, being its detection and evolution measurement a challenge for applied research. In this project we present a new methodology to detect corrosion and its evolution monitoring, based on the direct rebar–concrete interface changes measurement by the use of Optical Fiber Sensor (OFS) either a Fiber Bragg Grating (FBG) network or Optical Frequency Domain Reflectometry (OFDR) interrogation technique providing distributed strain measurement. A built-in pre-strain profile makes possible the detection of general and pitting corrosion with the same sensor. Accelerated corrosion tests were performed in an electrolytic solution under both pitting and general corrosion. A clear correlation is observed between the evolution of the spectral shift and the corrosion progress thus demonstrating the feasibility of this technique.
Multichannel Energy Harvesting Electronic Device for Structural Health Monitoring Systems
In this paper, the design, implementation and testing of a prototype for electric energy generation by collecting wasted ambient power sources coming from the mechanical vibrations (Piezoelectric Energy Harvesting PEH) is presented. This prototype has been specifically designed for applications where it is required to connect multiple transducers in order to obtain greater power levels. This design is based on a previous comparative performance analysis among all the existing autoharvesting circuits. The prototype has been tested in laboratory tests, with encouraging results. It has been proved that, though achieving power levels of a few milliwatts, the developed prototype has a significant potential for powering autonomous lowconsumption devices, such as Structural Health Monitoring (SHM) systems and other avionic sensors distributed in the structures of the aircrafts. A new integrated electronic device called PEH-PM24 (Piezoelectric Energy Harvester Power Management 24 channels) has been designed with capacity for 24 channels. The PEH-PM24 electronic system can be configured in many modes in order to obtain more or less voltage or current, in function of the number of piezoelectric transducers connected to the device or in function of the disposal of those transducers. One advantage of using these piezoelectric transducers is that they can be connected among themselves both in serial and in parallel configuration. In order to verify the appropriate operational performance of the manufactured electronic device, vibration tests have been carried out using several piezoelectric transducer sets. These tests have been performed in two separated stages. The first one was by employing only one transducer and the second with multiple transducers simultaneously. The experimental tests have been performed using a shaker where the piezoelectric transducers were placed in a cantilever configuration. Tests performed with multiple piezoelectric tuned transducers have been carried out in a mechanizing center yielding encouraging results. At present, research efforts are leaded to optimize the size, number and position of multiple transducers in large structures such as aircraft components.
Impedance-Based Structural Health Monitoring System Using Wireless Sensor Networks
This paper presents a novel wireless structural health monitoring (SHM) system based on a new method to detect damage in structures using the electromechanical impedance (EMI) principle. The proposed system is a wireless and low-power sensor network based on ZigBee composed by a link node to establish communication between remote monitoring center (host node and server) and multiple sensor nodes. The identification of damage is performed by simply comparing the variations of root main square (RMS) voltage from response signals of piezoelectric transducers.
Using Wireless Sensor to Solve the False Alarm of Earthquake Early Warning System
This research is focus on solving the false alarm problem of the on-site EEWS and a newly concept called “sub-regional EEWSâ€ is proposed. The sub-regional EEWS merges both the concepts of regional and on-site EEWS; the regional EEWS is accurate due to multiple data verification, and the on-site EEWS is fast by avoiding data communication. The sub-regional EEWS inherits the architecture of the on-site EEWS and extends considering the sub-site information from other positions; the concepts of wireless sensor, wireless sensor network and embedded parallel data pre- processing, are adopted in this system to avoid the delay time on data collecting and computing. The wireless sensing unit applied in the sub-regional EEWS is NTU-WSU whose basic architecture is from the prototype of Stanford University, WiMMS. This research will demonstrate a successful application of wireless sensor on earthquake early warning system, the working detail on system integration and the overall system performance of the sub-regional EEWS.
Characterization of Gallium Nitride Heterostructures for Strain Sensing at Elevated Temperatures
Structural health monitoring of physical stimuli such as strain within extreme harsh environments (high temperature, chemically caustic, high shock, high pressure, and high radiation) is currently limited. More specifically, critical structural components used in aircraft, combustion engines, and down-hole environments can utilize strain sensors to monitor structural state of health to prevent catastrophic failures and study mechanical fatigue within extreme environments. Unfortunately, traditional metal foil strain gauges have low sensitivity and silicon-based devices are limited to temperatures below 200 °C and benign environments due to junction leakages and material degradation. Primarily due to its large bandgap (3.4 eV) gallium nitride (GaN) offers a promising material platform for sensing within extreme environments because it is temperature resistant (electrically stable above 600 °C), chemically stable, and radiation-hardened. Additionally, when aluminum gallium nitride (AlGaN) is grown on GaN it creates a high electron density sheet at the interface known as a 2-dimensional electron gas (2DEG) layer. The gauge factor for AlGaN/GaN heterostructure devices is comparable to silicon devices and higher than metal foil gauges. This paper will examine how the GaN heterostructure devices are affected by elevated temperatures; this is done by fabricating 2DEG GaN resistors on cantilevers and applying known loads at the end while under temperatures from room temperature to 92 °C. The gauge factor of the device was found to increase in a linear relationship from -17.6, -52.6, and -80.7 for 25 °C, 48 °C, and 70 °C, respectively. However, the gauge factor appears to saturate at 70 °C only varying to -83.4 for 92 °C.
A Case-Study for Structural Health and Propellant Monitoring Technologies
This paper results in recommendations for selecting solid propellant monitoring (SPM) and structural health monitoring (SHM) methods and provides a case-specific assessment of these methods based on a 2.75 inch diameter guided missile demonstrator. Assessment criteria are determined based on the performance requirements and significant modes of damage. Specific metrics (trade-study variables) are then defined to objectify the technology-specific assessment process and outcome. In addition, the weight of each metric is determined as it varies with the design requirements of the chosen transition platform. The assessment then results in a down-selection within the technologies, concluding with one or a group of candidates, from which an SPM or SHM method is recommended. It is recommended that the SHM and SPM method features a developed graphical user interface (GUI), no requirements for an expert eye, fail-safety, portable and integrated packaging, little to no disruption to current manufacturing methods, and has been proven for composite cases and fielded. For this study, a case-embedded microsensor is recommended for the SPM approach and a PZT sensors approach is recommended for the SHM approach.
A 915 MHz Wireless Correlator Multi-Sensor System for SHM
Novel wireless RFID surface acoustic wave (SAW) sensor devices have been developed to address needs that often cannot be met by other technologies. These sensors are fully passive, and use only the interrogation energy to encode the device RFID and sensor information. This work will present results from an operational 915 MHz wireless SAW sensor system that demonstrates 8 SAW temperature sensors, received simultaneously from a single interrogation signal, having over 3 meters range. In addition, examples of room temperature reversible hydrogen SAW gas sensors with over 20 dB dynamic range and real-time response time (<1 sec) will be shown. An adaptive matched filter and synchronous correlator techniques are used at the receiver to extract the sensor information. The sensor examples and transceiver hardware and software approach will be discussed.
3D Photovoltaic Sensors for In-Situ Structural Health Monitoring of Advanced Composites
This paper reports on work developing an efficient 3D photosensor using Ti microwires and carbon nanotube yarns (CNYs). The 3D PV sensor construction is the basis of ongoing work towards embedded smart composites with intrinsic triboluminescent/mechanoluminescent (TL/ML) features. Nano-TiO2 coated microwires were used as working electrodes (WE). CNYs were twisted around the coated Ti microwire, which functioned to collect and transmit the photogenerated electrons from the completed WE. Attempts were made to optimize the interface between Ti-microwire and TiO2 microfilm with differing numbers of CNYs. The optimized TiO2 thickness was found to be approximately 20 μm. A silver wire was used as a control experiment to compare with the CNYs operating as the counter electrode (CE). The developed 3D photovoltaic (PV) microsensor displayed a photon-to-current conversion efficiency of 0.49%.
Wireless Acoustic Emission Sensor Powered by Microwave Energy
This paper presents the development of a wireless Acoustic Emision (AE) sensor powered by a microwave energy harvester. In addition to the wireless transponder consisting of two antennas and a frequency mixer, the wireless AE sensor is equipped with a voltage follower that couples the high impedance of the Piezoelectric Wafer Active Sensor (PWAS) to the low impedance of the frequency mixer. A microwave energy harvester was introduced to convert the microwave interrogation signal to a DC voltage for powering the voltage follower. The design and implementation of the RF powered wireless AE sensor is presented. The RF energy harvester, the voltage follower, and the wireless channel were characterized by the ultrasound pitch-catch and pencil lead break experiments.
Damage Detection in Composites by LAI-PZT Transducer
Composite structures are known for being susceptible to manufacturing defects and in-service damage. Since damage in composites is not always visually apparent, NonDestructive Techniques (NDT) such as ultrasonic imaging are often employed to detect and quantify damage [1]. Materials such as carbon-epoxy and fiberglass-epoxy plates are frequently used as structural elements in the both the civil and aerospace industry. Structure made of these materials display high acoustic impedances along the direction of the fibers but behave as Low Acoustic Impedance (LAI) materials in the transverse direction (i.e., through the composite’s thickness). It follows that LAI acoustic transducer are generally more appropriate to scan and detect damage/defects through the thickness in various types of anisotropic structures. When transducers are coupled in a direction orthogonal to the resonant mode they display significantly lower characteristic impedance than if coupled in the same direction. In some cases, this feature can eliminate the need for intermediate “matchingâ€ films and makes LAI transducer desirable for acoustic imaging applications. In this paper we introduce the use of low-cost, low effective impedance PZT transducer for damage detection in LAI structures. This manuscript also describes results from both numerical modeling and damage detection testing performed using a prototype transducer. In the future simple and economical transducers resonated in their transverse mode rather than the thickness mode could be used to detect damage such as matrix cracking and delamination of the composite structure.
Input Force Reconstruction Using Response Measurements
Accurate estimation of input excitation forces acting on a structure is significant for designing, controlling and diagnosing a structural system. The study of the conventional inverse method is to de-convolve the matrix equation to produce an estimate of an input forces based on the structural response and the impulse response. In addition to the conventional inverse method, the Kalman filter based tracking approach has been studied and developed for the identification of input excitation forces due to its accurate estimation in the consideration of measurement noise and modeling error. In this study, the modified Kalman filter input force identification using state estimation is used. Verification of the proposed approach is conducted through experimental study by considering a beam-like structure subject to external loading. Finally, application f the proposed method to identify the earthquake excitation force on Canton tower during Burma earthquake.
Displacement of a Skyscraper Using Various Types of Field Monitoring Data
Structure health monitoring (SHM) systems generates huge amount of data. Utlilization and interpret of the data are critical for the structural performance evaluation. Various data fusion and data mining techniques have been proposed, whereas real applications are still limited. This paper demonstrates a new use of strain data for displacement calculation. Based on the beam bending theory, the transverse displacement can be calculated from the strain (or curvature) of the beam. When the strain data of different segments are available, the transverse displacement of the entire component can be estimated. The technique is applied to the Canton Tower (previously known as Guangzhou New TV Tower), on which a long-term health monitoring system including over 300 strain gauges at various heights has been installed. The calculated displacements under normal temperature action and typhoon action are compared with those using GPS and a good agreement is observed. Error analysis shows that the accuracy of this technique is about 1 cm for the Canton Tower.
Influence Line-Based Damage Detection Technique for Long-Span Bridges
Numerous long-span cable-supported bridges have been built throughout the world in recent years. These bridges begin to deteriorate once built and continuously accumulate damage during their long service life. The growing popularity of comprehensive structural health monitoring systems (SHMS) in recently built longspan bridges has started a new trend of integrating SHMS and damage detection technology for real-time condition assessment of these bridges. This paper explores a novel damage detection technique based on stress influence lines (SILs) of bridge components, and validates the efficacy of the technique through a case study of the Tsing Ma Suspension Bridge. A mathematical regularization method is first introduced to identify SILs based on the in situ measurement of train information and train-induced stress responses in local bridge components. The first-order difference of SIL change is subsequently proposed as a damage index and applied to hypothetical damage scenarios. The comparison indicates that it is an accurate indicator of the damage location. Thus, the proposed SIL-based method offers a promising real-time technique for damage location in long-span bridges with comprehensive SHMS.
A Novel Bayesian Extreme Value Distribution Model of Vehicle Loads: Application to Nanjing 3rd Yangtze River Bridge
Vehicle traffic plays an important role in fatigue deterioration and overload leading to the collapse of bridges. The monitored data show that occurrences of vehicle loads are correlated. Additionally, it is more reasonable to employ the tail region of a distribution when estimating extreme loads. A novel de-correlated tailbased extreme value (EV) distribution model is proposed in this paper. Moreover, a Bayesian form of this new model is constructed, and an extension of this model, the Confidence Index (CI), is defined and may be promising for applications. The monitored vehicle weight on the Nanjing 3rd Yangtze River Bridge is used to demonstrate that the proposed tail-based de-correlated EV model predicts the extreme load more accurately than traditional methods and that the Bayesian approach can further increase the precision of this estimate. Finally, the calculated CI of the complete prediction process offers a comprehensive guideline for the estimate precision.
Design of Structure Health Monitoring System for Nanjing 4th Yangtze River Bridge
Reviewing the constructed structure health monitoring (SHM) system of Nanjing 4th Yangtze River Bridge, the self-developed system frame, hardware and software scheme of this practical project are systematically discussed in this paper. This paper mainly focuses on the technology and features of the Nanjing 4th Yangtze River Bridge SHM system, including system design, application of sensors, data acquisition, data processing, data real-time display and early-warning, data analysis, structure safety assessment. The data acquisition and transmission subsystem based on the industrial Ethernet technology were employed, with IEEE1588 and GPS time service receiver technology. The objectives are to establish the structure safety monitoring and status evaluation system to monitor the structure responses and working conditions in real time and to analyze the structure working status with information obtained from the real data. It will also support scientific decision-making for the bridge maintenance. The performance of the system has achieved the pre-set objectives, ensuring the longterm high reliability, real time concurrence and advanced technology of the SHM system of Nanjing 4th Yangtze River Bridge. It is the first time to implement such a system in domestic China, which provides a reference for the design of an SHM system for a long-span bridge.
Vehicle-Bourne Autonomous Railroad Bridge Impairment Detection Systems
Timber bridges constitute approximately 30% of current railroad bridge inventories in North America. Inspections of these structures involve visual assessments of the observable condition of individual components that comprise the bridge system. Special inspections may call for field personnel to observe a bridge under load: that is, while a train is crossing. Individually monitoring large numbers of timber bridges can be resource intensive, and the use of electro-mechanical sensors such as strain gages, displacement transducers, load cells, accelerometers, and related instrumentation is limited to rare exceptions to enhance visual inspections; it is done “as deemed necessaryâ€ on a “bridge-by-bridgeâ€ basis. The fitness of a timber bridge strongly correlates to its structural response under load. Specifically, strength deficiencies and other impairments in a timber bridge often result in degradation in member and connection stiffness, and such degradation can be detected under laboratory conditions. One specific impairment scenario common to timber bridge systems is timber stringer fatigue degradation. Models of timber bridges subject to varying levels of impairment are presented. Preliminary quasi-static MATLAB models, SAP2000 models, and refined LS-DYNA models were developed to investigate the effects on structural behavior resulting from typical timber bridge impairments such as stringer fatigue. Analytical results indicate significantly increased deflections for impaired stringers. For example, impaired bridges with an effective bending stiffness approximately 1/3 that of unimpaired bridges may experience nearly 100% increase in deflection. This paper presents a Vehicle-Bourne Autonomous Impairment Detection System that will facilitate detecting structural impairments in timber railroad bridges using data gathered from rail vehicles that cross the bridges. Deflection profiles are related to wheel path accelerations which serve as the primary diagnostic data stream with which to evaluate a timber bridge. Accelerations can be measured via instrumented rolling stock, and the data streams can be processed by a Structural Impairment Detection System (SIDS) comprising arrays of competitive artificial neural networks.
Structural Impairment Detection of Tunnel Linings Using Ultrasonic Sensors
Tunnel inspection is a challenging problem since they typically service high volume traffic and operate in aggressive environments. Keeping tunnels open during inspection and minimizing tunnel closures and user delays must be carefully balanced with the need to conduct detailed inspections to ensure the safety of drivers. This paper describes tunnel lining impairment detection and evaluation that is performed in the Hanging Lake Tunnel near Glenwood Springs, Colorado, using aircoupled ground penetrating radar (GPR) and ultrasonic tomography (UST). Potential regions of interest to monitor are first identified using high-speed air-coupled GPR and visual inspection, and then the UST technique is used manually. This study shows that this particular combination of NDE techniques is a powerful tool for monitoring and assessing the condition of tunnel linings and can detect potential anomalies such as delamination, depth of surface cracks, reinforcement depth and layout, and lining thickness. Since typical tunnel lining evaluations only involve visual inspection and monitoring, distress observed is often the result of underlying damage that has gone undetected by the human eye. For this reason, structural impairment detection is needed to complement visual inspection in order to detect the beginning stages of damage before it inhibits serviceability requirements, or worse, becomes a public safety issue. When these early stages are identified by NDE methods before significant damage has taken place, preventive actions can be recommended that will arrest the development of further damage.
Implementation of a Structural Impairment Detection System on a 100 Year-Old Bascule Bridge
Critical instances of reliable bridge functionality arise in situations where waterway transportation conflicts with bridge traffic. Bascule bridges, or drawbridges, are one class of movable bridges that can be designed to accommodate both flows of traffic. While popular, heel trunnion bascule bridges have not been without problems and failures. Bascule bridges are subject to multiple cycles of opening and closing on a daily basis, and thus structural members undergo large stress ranges. This paper presents an implementation of a Structural Impairment Detection System (SIDS) that incorporates finite element modeling, instrumentation, and continuous evaluation of a testbed bascule bridge structure. Heel trunnion bascule bridges experience significant stress ranges in critical truss members. Finite element modeling of a testbed bridge provided (1) an estimate of nominal structural behavior, (2) an indication of types and locations of possible impairments, (3) a basis for the design of an instrumentation program appropriate for detecting possible impairments, and (4) data streams with which to train a neural SIDS. Analytical modeling was initially performed in SAP2000 and then refined with ABAQUS. Finite element models and experimental observations indicated maximum stress ranges of approximately 22 ksi on main chord members of the counterweight truss. Instrumentation used to observe structural behavior included electrical resistance strain gages, clinometers, and quadrature encoders. Ultimately, data streams from the testbed bridge are autonomously recorded and interrogated by competitive arrays of artificial neural networks for patterns indicative of specific structural impairments. A quasi static array of competitive neural networks was developed to provide an indication of the operating condition at specific intervals of the bridge’s operation. Based on neural algorithms trained on modeled impairments, the testbed operates in a manner most resembling one of two operating conditions: 1) unimpaired, or 2) an impaired member embedded at the southeast corner of the counterweight.
Structural Health Monitoring on Cracked Railway Axle Journals Using Ultrasonic Phased Array Technique
The fatigue-prone nature of railway vehicle axles is a concern due to increasing vehicle loads and speeds. The failure of one axle has the potential to derail an entire train. Derailments cause a great danger to the public, can be life threatening, and may lead to thousands of dollars in repair costs and rehabilitation. The most idealistic way to prevent such a disaster is to inspect railway cars at every station. Unfortunately, this is unrealistic due to the cost and time associated with inspection. The middle region of the axle is the most fatigue critical region, as it experiences the largest stress cycles. Previous studies have shown that the axle body can be inspected using a non-contact ultrasonic technique on moving trains. Another fatigue critical region is the axle journal. Accessibility to the axle journal is limited since the journal is covered by the bearing, while surrounding areas are covered by the bearing cap and wheel. The main challenge in this research is to overcome the limited accessibility using ultrasonic techniques. Structural health monitoring on the railway axle journal using ultrasonic phased array (UPA) is introduced in this paper. UPA provides a high probability of detection (POD) and allows for high-speed scanning from a single inspection point. The technology also has a greater flexibility that allows the inspection of complex geometries. UPA transducers are attached on each railroad car axle as part of a continuous monitoring system. A full-scale railway axle specimen with multiple artificial cracks is used in the experiment. An image processing technique and a pattern recognition algorithm are applied to the results obtained from the transducers. The grey-level segmentation technique is used to clarify the resulting C-Scan images, and a supervised learning algorithm, Support Vector Method (SVM), is used to classify the axle condition. The incorporation of post-processing techniques minimizes human involvement. This research will contribute toward the development of a reliable and accurate inspection method to improve railway safety.
SHM System of the Roof Structure of Sports Arena “Oliviaâ€
The paper presents a structural health monitoring system of the Sports Arena “Oliviaâ€ in Gdansk, Poland. The system was installed because of doubts about the roof stability due to observed buckling of the steel truss members. The SHM system was based on nonlinear FEM models used for detection of type of near failure modes of the roof. The application of detailed FEM model in current state updated on data from in situ loading testes allowed to trace large number of structural parameters necessary for precise identification of the roof structure condition.
A Competitive Array of Artificial Neural Networks for Use in Structural Impairment Detection
In light of aging infrastructure, an opportunity exists to develop improved systems for monitoring and evaluation of infrastructure systems. Analytical modeling and experimental observation of an instrumented structure provide insight into to structural behavior. Such insight enables engineers and decision makers to maintain and monitor their structural systems. Accessible and practical dissemination of evaluations resulting from structural monitoring is a critical step in the successful management of infrastructure. As such, Structural Impairment Detection Systems (SIDS) target the detection of specific, expected impairments and provide succinct reports to decision makers. This paper details a methodology for use in Structural Impairment Detection (SID) that incorporates competitive arrays of artificial neural networks. Traditional modeling and instrumentation of a bridge structure are coupled with an advanced pattern recognition algorithm comprising arrays of competitive neural networks. Unknown variations in loading, material composition and interaction, and structural geometry make it difficult to model a physical structure exactly. While matching exact values for deflections or stresses between a physical structure and a computer model is not realistic outside of a simple laboratory experiment, one does expect that the overall behavior of a structure can be captured in a structural model, and trends in behavior can be validated. For this reason, neural networks are one appropriate approach to the problem of Structural Impairment Detections. Neural networks can be trained to examine trends in structural behavior and identify patterns that correspond to target impairments. The approach presented in this paper provides several competing, untrained networks the opportunity to be trained on individual data streams. The competition is carried out by implementing an orthogonality criterion that examines the output response of an individual network and the target response. This approach increases the speed of training and the ability of the neural networks to classify new data streams. A 94% correct classification rate on simulated data streams representing a complex physical draw bridge structure is achieved.
Characterization of CFRP Laminates: Linear and Nonlinear Acoustic Measurements of As-Fabricated Materials
Changes to the microstructure of a composite laminate during the early stages of fatigue are quite subtle, particularly with respect to the capabilities of most nondestructive evaluation methods. Additionally, the nominal variation of microstructure within the laminate and this variations effect on nondestructive evaluation signals further limits the sensitivity of any given method to fatigue damage. Here we report on the level of variation in ultrasonic response of asfabricated CFRP laminate samples, in unidirectional, cross-ply and quasi-isotropic layup configurations. In additional to the standard immersion back wall pulse echo amplitude c-scans, we show results of precision attenuation and backscatter amplitude measurements in c-scan formats, as well as nonlinear ultrasonic resonance spectroscopy (NRUS) results. This material was based upon work performed at Iowa State University and supported by the US Army Research Office.
Uncertainty Quantification of Multiscale Composite Damage Initiation and Progression
Uncertainty is inherent in composite materials, and causes several challenges when modeling and characterizing complex structures. There are uncertainties present at each length scale in composites, and quantifying these uncertainties is necessary in order to accurately model the mechanical response and damage progression of these materials. The ability to exchange information between length scales permits multiscale models to transport uncertainties from one scale to the next. Limitations in the physics and errors in numerical methods pose additional challenges for composite models. By replacing deterministic inputs with random inputs, stochastic methods can be implemented within these multiscale models making them more robust. A multiscale sectional model is used due to its efficiency and capacity to incorporate stochastic methods with little difficulty. A Latin hypercube sampling technique is used, due to its reported computational savings over other methods such as a completely random Monte Carlo simulation. Within this multiscale modeling framework, a progressive failure theory is implemented using these stochastic methods and a modified Hashin failure theory. A stochastic multiscale model with a progressive failure theory successfully captures variations of mechanical properties in composite materials.
Damage Precursor Investigation of Fiber Reinforced Composite Materials Under Dynamic Cyclic Loads
Glass-epoxy composite structures are frequently used in many aerospace applications, such as landing gear struts, satellite frames, aircraft nose cones, and interior fuselage stiffeners. In glass fiber/epoxy composite systems, damage precursors such as micro-cracking have significant implications in impacting the performance and life span of the structure. As a consequence, it is necessary to gain a better understanding of the response under fatigue loading. The focus of this study is to identify the stress levels responsible for crazing, micro-crack formation and coalescence into macrocrack in S2 Glass Cycom381 Epoxy matrices (8 plies woven). Fatigue tests are performed at 1, 100, 1,000, 10,000, 100,000, and 1,000,000 cycles under tensile loading conditions (R=0.1). Strategies for monitoring the damage prior to, during, and after cyclic loading conditions include ultrasound and acoustic emission (AE) non-destructive evaluation techniques. AE is used during the fatigue loading tests to identify and localize real-time the presence of rupture, delamination, and crack formations. The high frequency ultrasound immersion technique is conducted for high fidelity interlaminar evaluation prior to fatigue testing. After each test, an examination of the fracture surface is conducted by several image characterization techniques including optical microscopy and Scanning Electron Microscopy (SEM). By this systematic evaluation, the phenomena of crazing, the propagation of micro-cracks, delaminations and other factors that lead to catastrophic failure are investigated. At the end of this study, an empirical framework is developed that maps the relationship between the sizing and location of the damage pre-cursors and the corresponding matrix/fiber degradation mechanisms.
Overview of the Use of Acoustic Emission and Electrical Resistivity for Damage Detection in Ceramic and Polymer Matrix Composites
Micro-fracture events such as matrix cracks and fiber breaks are critical phenomena to monitor for ceramic and polymer composites in order to assess the health of the material. For example, in ceramic composites, low stress matrix cracking leads to life-limiting conditions at elevated temperatures in the presence of oxygen or water vapor environments. Modal acoustic emission and electrical resistivity are two techniques which have been used to monitor matrix cracking as well as other microfracture events in ceramic and more recently polymer matrix composites which can then be incorporated into thermo-mechanical behavior models to determine residual properties and/or life-expectancy. The keys to effective use of acoustic emission, when monitoring damage in ceramic and polymer composites, are the ability to accurately locate the events and effectively quantify the waveform properties to relate waveforms to physical sources. Several location techniques have been explored with different levels of success. Waveform frequency content and energy have been used to accurately quantify sources. Finally, an example of acoustic emission and electrical resistance monitoring for a SiC/SiC composite is shown to highlight the potential for the use of electrical resistance as a health monitoring technique in this composite system.
Monitoring Transverse Matrix Cracking in Composite Laminates Using Ultrasonic Guided Waves
In composite materials matrix cracking initiates first and promotes the development of more dangerous damage such as delamination and fiber breakage. The objective of this investigation is to determine the quantity of matrix cracks in a composite laminate rapidly and in-service using built-in piezoelectric actuators and sensors. A diagnostic method was proposed which compared the sensed signal before and after the presence of matrix cracks and related these signal changes to crack density. A crack density quantification model was developed that uses signal changes and theoretical laminate stiffness parameters. The model was tested on two layup configurations and its crack density estimates matched experimental crack density values within established error bounds.
Nonlinear Ultrasonic Time Reversal Imaging for Closed Crack Location in Metallic Structures
Initial crack in metallic materials inclines to be closed at rest. Such imperfection generally fails to be diagnosed and located with traditional linear ultrasonic techniques because ultrasonic waves penetrate the contact area of the closed crack. In this paper, an imaging algorithm based on nonlinear ultrasonic time reversal to detect closed crack using Lamb wave signals which are transmitted and received by surface-bonded piezoelectric transducers array is proposed. We develop a three-dimensional finite element model with a closed crack, the model makes use of local node splitting and nonlinear spring elements at the crack interface. Once excitation voltage is able to overcome the threshold to open the contact, the stiffness of the nonlinear spring will be changed and the nonlinear response which can be detected using the scaling subtraction method is produced. Applying a low amplitude excitation voltage as a comparison signal which, linearly rescaled in amplitudes, is not equal to the signal recorded at larger excitation amplitudes, it indicates the nonlinear response produced. The correlation coefficients between the original excitation signal and reconstructed time-reversed signal are calculated to define the damage index for individual sensing path, which are used to develop an imaging algorithm to locate the closed crack in the metallic structures. The results of the numerical simulation demonstrate that incident wave singles and their reconstructed singles can be used to accurately detect closed crack.
Monitoring of Friction Condition Using a System Identification Technique
ï€ In this paper, the effect of friction force on a feed-drive mechanism is analyzed. The feed-drive mechanism is controlled by a commercial servo control system which consists of position and velocity feedback loops. To characterize the relationship between the friction forces and the sliding velocities, the LuGre friction model is adopted and experiments under constant sliding velocities are performed to identify the parameters associated with the static friction force, the Coulomb friction force, the viscous friction force and the Stribeck effect. To verify the identified parameters, the torque commands obtained in the experiments are compared with the simulation results. It is found that since the friction forces are the functions of sliding velocities, they form a feedback loop in the velocity loop and will interact with the PI servo controller. Besides, the linear effect of friction forces including the Coulomb friction force and the viscous friction force can be captured and compensated by the PI servo controller. However, the nonlinear Stribeck effect could not be well compensated via the PI control architecture and it needs auxiliary compensation technique to achieve high moving accuracy. The simulation results in this paper could provide a fundamental basis for further development about the compensation technique for the nonlinear effect of friction forces.
Dynamic Monitoring and Study of Shaft Rotating Deviation Error Using Developed MEMS Gyroscope-Based Sensing Module
This paper presents the development of a MEMS gyroscope-based sensing module and the experiments to test the sensing module in two different rotating mechanisms. The developed module has no wire connection for power supply and signal transmission, which avoids the wire twisting problem during spindle rotating test. The module is then firmly secured on an aluminum-made disk and a slender spindle driven by a servo motor to test the dynamic responses of the rotating mechanisms. For the first case, the angular velocities measured from the sensing module are verified the accuracy through the encoder readout of the driving motor. For the second case, the sensing signals are acquired when the spindle is driven with different speeds. A loading added on the front end of the spindle is also compared to the situation without a loading added. The experimental results are further analyzed and discussed to find the corresponding dynamic characterization of the tested spindle.
Development of AE Signal Based Tool Breakage Prognosis System in Micro Gun Drilling
The AE signals were analyzed in this study for monitoring tool conditions in micro gun drilling to improve the reliability of tool condition monitoring system in micro gun drilling. The AE signals corresponding to the conditions before and during tool breakage were analyzed first based on the time and frequency domain features, followed by the development of a prognosis system for the micro gun drilling process. The system consists of three modules, including a signal transform module for feature generation, a feature selection module for selecting proper features closely related to the monitored event, and a classifier for distinguishing the condition just before tool breakage from the others. To collect signals for the feature analysis and the system development, an experiment was conducted on a Swiss type turning machine. A micro drilling tool with the size of 0.9mm in diameter and 125mm in length was used in drilling the stainless steel. At the same time, an AE sensor was installed on the tool holder to collect the AE signal. During the deep hole drilling, the gun drill was fixed on a tool holder and the stainless workpiece rotated to generate the motion for micro drilling. The results show that the feature of AE signal can be observed to be changed not only during tool breakage, but also before the tool breakage occurring. With properly selecting the features closely related to various tool conditions, the condition before tool breakage can be detected successfully by the developed prognosis system.
Development of an Intelligent Shaft Fault Diagnosis System for Machine Tools
Aiming at reducing cost and time of repair, condition-based shaft faults diagnosis is considered an efficient strategy for machine tool community. While the shaft with faults is operating, its vibration signals normally indicate nonlinear and non-stationary characteristics but Fourier-based approaches have shown limitations for handling this kind of signals. The methodology proposed in this research is to extract the features from shaft faults related vibration signals, from which the corresponding fault condition is then effectively identified. Besides Fourier Transform, two new algorithms are used to extract the feature of signals, empirical mode decomposition (EMD) and multi-scale entropy (MSE). With an incorporation of EMD method, the model applied in this research embraces some characteristics, like zero-crossing rate and energy, of intrinsic mode functions (IMFs) to represent the feature of the shaft condition. The other method called MSE is used to calculate the entropy of multi-scale of the signal. The curve of MSE can be used to identify some defect model of shafts clearly. The conventional approach for online monitoring of a machine’s health based on linear time-frequency analysis has its limitation, as the mechanical vibration signal is nonlinear and non-stationary in nature. Multi-scale entropy (MSE) is a recently developed method for nonlinear time series analysis, which quantifies a system’s complexity. EMD-based and MSE-based methods were implemented to develop a diagnosis system in this research. In the buildup stage a knowledge ware is created from the database compiled from the existing defect models. Finally, diagnosis system is a monitor system to be implemented in a machine tool manufacturing company to verify the ability.
Ball Screw Health Monitoring Using Ball Pass Frequency Spectra
In a precise ball screw feed drive system, the backlash caused either by the manufacturing clearance between ball and ball track or by the deflection backlash induced by the external force can be eliminated by a preload. In practice, the ball screw is deemed as expired and required to be replaced when the preload loss occurs. A technique based on the method of Angular Velocity Vold-Kalman Filtering Order Tracking (AV VKF-OT) for monitoring the change of the ball pass frequency (BPF) in order to determine the onset of preload loss of a ball screw in a feed drive system is proposed. The advantage is that the vibration caused by sources other than the ball screw itself can be excluded in the measurement. Moreover, the signal that correlates directly to the moving ball and thus the preload can be extracted with a better signal to noise ratio. Tests were conducted on ball screw feed drive systems with different temperature for the performance assessment of the proposed method. It concludes that the preload loss detection using AV VKF-OT can be implemented whenever the ball screw feed drive system is warming up or have been operated for some time if the temperature varies less than 10.8ï‚°C.
Multi-Physics Modeling and Simulation of a Slotted Patch Antenna for Wireless Strain Sensing
This research studies multi-physics simulation of a slotted patch antenna sensor. In our previous work, a folded patch antenna was designed for passive wireless strain and crack sensing. When experiencing deformation, the antenna shape changes, causing shift in electromagnetic resonance frequency of the antenna. The wireless interrogation system utilizes the principle of electromagnetic backscattering and adopts off-the-shelf 900MHz radiofrequency identification (RFID) technology. In this research, a slotted patch antenna sensor is designed, while maintaining antenna resonance frequency to be around 900MHz. The slotted antenna detours surface current using slotted patterns, so that the electrical length is kept similar as previous folded patch antenna sensor yet sensor footprint is reduced. To accurately describe both mechanical and electromagnetic behaviors of the antenna sensor, a multi-physics coupled simulation approach is pursued. A multiphysics finite element model uses the same geometry and meshing for both mechanical and electromagnetic simulations. Because electromagnetism has little influence on the mechanical behavior of the sensor, displacement field and electrical field can be solved separately using segregated steps. Known as sequential coupling, the solution method involves two or more analyses, each solving for a different physical field. The mechanics-electromagnetics coupled simulation is implemented
Characterizing the Self-Sensing Properties of Photoactive P3H T-Based Nanocomposites
In this study, the strain sensing properties of poly(3-hexylthiophene) (P3HT)- based films were characterized using absorption spectroscopy and photocurrent generation. In particular, the objective was to reveal how photocurrent generation and bulk film strain sensitivity were influenced by morphological changes of the bulk heterojunction (BHJ) layer, which consisted of P3HT and fullerene-based [6,6]-phenyl C61 butyric acid methyl ester (PCBM) polymers. Eight different P3HT:PCBM sets were prepared, each with a unique P3HT:PCBM concentration ratio. The absorption spectra of the eight different sample sets were measured, and their sensitivity to strain was characterized. Then, their optoelectronic properties were investigated by measuring their photocurrent generation under light on/off conditions. Lastly, the films’ photocurrent time histories were measured as strain was applied to them. It was found that strain-sensitive photocurrent was observed as a result of strain-induced changes in the light absorption properties of the P3HT:PCBM BHJ layer.
Dual Mode Sensing on Grout Structures with Piezoelectric Sensors
Monitoring of cracking in low-strength grout structures is of high interest to owners and regulatory agencies associated with infrastructure safety. Due to the variety of deterioration sources and locations of defects in infrastructure materials, there is currently no single method that can detect and address the potential sources globally. In this paper, we present a dual mode sensing methodology that attempts to integrate passive acoustic emission (AE) and active guided ultrasonic wave (GUW) inspection methods based on the use of permanently installed piezoelectric sensors (PES). Active or growing cracks emit AE waves that arise from the rapid release of strain energy. Analysis of these recorded signals provides early stage information about the cracking source with very high sensitivity. The “activeâ€ SHM approaches, on the other hand, use one sensor in the array to generate interrogation waves and the others as the wave receivers. By that means, quantitative information regarding the structural state of health and integrity can be extracted. A set of proof-of-concept tests are performed on a small grout specimen with two types of PES transducers. Two array imaging algorithms are developed for AE source localization and active damage detection, respectively. The methods demonstrated in this investigation provide promising means for imaging of cracking events both passively and actively.
Mobile Sensors in Bridge Health Monitoring
This paper will address a main concern in using mobile sensor data for SHM. Mobile sensors can offer comprehensive data similar to a “maximum fixed sensor networksâ€ while keeping the simplicity of “minimum fixed sensor networksâ€. However, the data is expected to have an undesirable incompleteness feature and would not be readily compatible with modern SHM algorithms, e.g. feature extraction, data compression, system identification, traffic monitoring, reliability analysis, etc. requiring an additional model to compensate for incomplete data. This data-collection technique calls for a means to move sensors over a vibrating bridge, which could be implemented in a number of different ways. One example includes a vehicle (with onboard sensors) traveling over the roadway of the bridge to scan its vibrations in space and time.
Quadrotor-Based Wall-Climbing Robot for Structural Health Monitoring
As civil structures become massive and high, the inspection and maintenance become more important. However, most of the conventional inspection methods are labor-intensive and costly due to its high risk. To solve these problems, the need for wall-climbing inspection robots has been emerged. Infrastructure-based wall-climbing robots have been studied for a long time to inspect and maintain an outer wall of the building with high payload and safety. However, the infrastructure installed on the wall is not preferred because it hinders appearance of the building. Consequently, the architects don’t prefer the infrastructure-based wall-climbing robots. It’s one of the reasons why the wall-climbing robots that do not utilize additional infrastructure are gaining attention recently. In the conventional wall-climbing robots, they stick to the wall using adhesion mechanisms such as magnetics, vacuum system, or adhesion materials. The robots do not use the infrastructure for wall-climbing, but most of the technologies are in the laboratory level since the payload, safety, and maneuverability are not satisfactory. To overcome these problems, a quadrotor-based wall-climbing robot is proposed in this paper. The robot is designed for both wall-climbing and flying with four rotors and wheels coated with adhesive material. It can fly using the thrust forces generated by four rotors. Also, it can stick to the wall with the same thrust forces and then climb the wall with four wheels. The wall-climbing robot can stabilize its position even if it is suddenly detached from the wall by unexpected disturbances while climbing the wall by using the flying capability. The maneuverability and safety of the robot have been improved by using the four rotors and wheels coated with adhesive material. The feasibility of the main concept was verified through simulations and the prototype of the robot has been made. The mechanism of the proposed robot has been demonstrated on a wall of a building.
Performance Analysis for CNT-Based SHM in Composite Structures
This paper presents results from a recent set of experiments devised to benchmark performance of a novel carbon nanotube (CNT) based approach for SHM in composite structures. CNT were bonded to cured carbon-fiber laminates using surfacing film, with electrodes embedded at either end. Five of each specimen was then subjected 3 types of damage: impact, cut slots and 4-point bending. For each type of test, resistance measurements were across the CNT network following each damage step, and during the 4-point-bending tests data was also collected under static load in addition to the unloaded state. Results indicate that the CNT networks exhibited little to no resistance change under nominal operating conditions, however significant resistance increases could be measured following events that permanently affected the composite laminates. CNT-based SHM has the potential to be a sensitive and reliable “witnessâ€ style damage detection method, while only introducing < 20 g/m2 mass and < 100 μm in thickness.
Experiments on Structural Displacement Monitoring Using ViSP (Visually Servoed Paired Structured Light System)
As structural displacement is considered as one of important indicators to assess structural conditions, various displacement monitoring systems or methods have been researched. To estimate 6-DOF relative displacement regardless of environmental changes, a visually servoed paired structured light system (ViSP) was proposed in the previous study. The system is composed of two sides facing each other, each with one or two lasers, a camera, a screen, and a 2-DOF manipulator. A total of three lasers project their parallel beams to the screen on the opposite side and the camera near the screen captures an image of the screen. The translational and rotational displacements each in three axes are estimated by calculating positions of the projected laser beams and rotation angles of the manipulators. Since the distance between the camera and the screen is short, typically less than 20 cm, the system is robust to environmental changes such as weather or illumination changes. By controlling the visually servoed manipulator which holds the lasers, the 6-DOF displacement can be estimated regardless of a magnitude of the displacement despite the limited screen size. To validate the performance of ViSP, a field test using a five-story steel frame building structure was carried out. The prototype of the system was built and installed on the building structure in vertical direction. The movements of the structure under external loads were monitored by using ViSP and then the results were compared with reconstructed displacements from a 3-axes accelerometer. The estimated translational displacements from ViSP agree with those from the accelerometer and it is found that the system has a strong advantage that it can estimate not only the translational displacements but also the rotational displacement with low noise level. Through the experimental results, the performance of the system and its applicability to real civil structures were verified.
Quantifying Damage Precursor Using Microcontinuum Physics
It is quite challenging to quantify the precursor to damage state in composite materials. Interaction between intrinsic material state and ultrasonic wave signal is evident from many literatures. But, quantifying such state, before their manifestation, has not been investigated properly. It has been found that the fluctuation in time of flight (TOF) of dominant wave modes in structures are possible to be detected, if measured using equipment with the capability to measure at least nano to pico second fluctuations. Therefore, it is imperative to quantify such fluctuations by understanding of the physics behind such changes. Microcontinuum approach provides the tool to explain the nano to pico second fluctuations of wave modes at early stage of damage. In this paper Bi-Helmholtz type nonlocal parameters are used to quantify the damage state in carbon fiber composite specimens and subsequently the state is verified using high frequency scanning acoustic microscopy imaging. Two carbon fiber composite specimens were tested under fatigue loading and the damage state is quantified every 500 cycles. It has been found that before actual visible damage the new damage parameter is able to indicate the precursor to damage state.
Reference-Free Fatigue Crack Detection Using Nonlinear Wave Modulation
This study presents a reference-free fatigue crack detection technique using nonlinear wave modulation. Two independent input signals are generated using two surface-mounted lead zirconate titanate (PZT) transducers; a low frequency (LF) signal and a high frequency (HF) signal. The presence of a structural nonlinearity, such as a fatigue crack, can provide a mechanism for nonlinear wave modulation, and create spectral sidebands around the frequency of the HF signal. A signal processing technique combining linear response subtraction (LRS), synchronous demodulation (SD) and continuous wavelet transform (CWT) filtering is developed to extract only the crack-induced spectral sidebands. Actual fatigue crack is detected without any data obtained from intact condition is developed using sequential outlier analysis. Finally, the robustness of the proposed technique is demonstrated under temperature and loading variations at two distinct sensing locations.
Nondestructive Detection of Nonlinear Behavior of Plain and Polymer Concrete Under Cycling Loading
Polymer concrete is a composite material that uses polymeric resin as aggregate filler. This type of concrete is strong, durable, non-absorbent and chemical resistant. Under cyclic loadings pre-existing cracks inside both conventional concrete and polymer concrete may grow and cause catastrophic failure of the structure. Structural failure under cyclic loading is also known as the fatigue failure. This study aims to investigate the behavior of plain and polymer concrete under the cyclic loading or fatigue. The specimens used for this study included cylindrical specimens of 3 inch diameter and 6 inch in length. They were subjected to compressive load for various numbers of cycles. The applied load was gradually increased. After the loadingunloading cycles, nonlinear impact resonance acoustic spectroscopy (NIRAS) test was carried out on the samples using instrumented impact hammer. The degree of nonlinearity in the concrete specimens was measured by recording the shift in the resonance frequency as the impact energy increased. The results indicated that for conventional concrete specimens the material nonlinearity increased with the number of loading cycles and the applied load magnitude. However, for polymer concrete it remained almost unchanged. Experimental results from conventional plain concrete and polymer concrete specimens were compared.
Correlation-Based Imaging Algorithm for Bolt Loosening Monitoring on Realistic Aerospace Structure
In the present study, a correlation-based imaging technique called « Excitelet » is proposed to monitor bolt loosening on an aluminum lap-joint, representative of an aircraft component. The principle is based on guided wave generation and sensing using a compact micro-machined piezoceramic array and measurement of reflections induced by potential damage. The method uses a propagation model to correlate measured signals with a bank of signals and imaging is performed using a round-robin procedure (Full-Matrix Capture). This methods allows taking into account the transducer dynamics and finite dimensions, multi-modal and dispersive characteristics of the material and complex interaction between guided wave and damage. Experimental validation has been realized on an aluminum lap-joint instrumented with a compact linear piezoceramic array of 8 circular elements of 3 mm diameter each. The imaging algorithm is applied to detect the partial and complete loosening of one of the bolts. Imaging results obtained using both A0 and S0 modes at 300 kHz are presented for each loosening condition. It is demonstrated that variations in the bolt torque can be accurately detected, while the correlation level indicates the level of reflected energy, related to the loosening. An accuracy of approximately 5 mm on damage location estimation can be achieved, demonstrating the possibilities of the correlation-based imaging technique for damage monitoring of complex aerospace structures.
Multimodal Non-Destructive Evaluation Approach for Damage Quantification
The prevailing materials-by-design concept for advanced applications in energy, automotive, aerospace, mechanical and civil industries greatly depends on the ability to identify and quantify critical relationships between reversible/irreversible (micro) structural changes and deformation/damage mechanisms. . Despite the advances in this field, the need for actual quantified and cross-validated NDE information across length scales is of paramount importance for i) understanding fundamental aspects in material development, e.g. plasticity and fracture, ii) linking developing damage with applied loading conditions from both environment and structural design, and iii) continuously monitoring the operation of critical infrastructures e.g. in commercial aircrafts. This paper presents a framework for damage quantification in fiber metal laminates such as GLARE used primarily for aerospace applications. This approach is based on the systematic mechanical testing of its constituents (metals, fibers and fiber-reinforced polymers) coupled with the synchronous use of two complimentary NDE methods such as Acoustic Emission (AE) and Digital Image Correlation (DIC) to reliably track the two important stages related to damage, i.e. damage initiation and subsequent (and progressive) damage development. Additionally, numerical simulations are performed to capture important aspects of the mechanical behavior of GLARE and in turn assist the interpretation of the obtained experimental results. In addition to the experimental and computation work, an information post-processing scheme that is based on smart filtering and damage sensitive feature extraction is presented to address the challenging task of damage identification in GLARE.
Characterization of Fatigue Damage in Adhesively Bonded Lap Joints Through Dynamic, Full-Spectral Interrogation of Fiber Bragg Grating Sensors
In this work we demonstrate that dynamic, full-spectral scanning of FBG sensors embedded in the adhesive bond can identify changes in bond quality through the measurement of non-linear dynamics of the joint. The lap joint specimens were then subjected to fatigue loading, with regular interrogation of the FBG sensors at selected load cycle intervals. The FBG data was collected during vibration loading of the lap joint to represent an in-flight environment. Changes in the lap joint dynamic response, including the transition to non-linear responses, were measured from both the fullspectral and peak wavelength FBG data. These changes were correlated to initial manufacturing defects and the progression of fatigue-induced damage independently measured with pulse phase imaging and visual inspections of the failure surfaces. Finite element simulation of the lap joint dynamic response verified that the FBG response was due to the fatigue-induced damage.
Non-Collinear Inspection for the Detection of Damage Precursors
Fatigue and creep damage are two of the most common modes of failure in engineering structures, and both are extremely difficult to detect in their early stages. Conventional ultrasonic approaches have to date been unable to detect the early stages of these damage types, due to the lack of any apparent changes to the gross structure on the scale of the wavelength. An alternative to conventional NDT techniques is the use of nonlinear techniques, a series of approaches that rely on the nonlinear nature of material responses. This paper examines how a non-collinear mixing method may be employed to measure the inherent nonlinearity in a material and hence detect precursors. The fundamental theory is described and it is shown how this can be used to predict the nature and amplitude of the generated response. Experimental results are given that indicate the sensitivity of the approach to damage precursors. This is then used to show how a pure amplitude measurement is perhaps not the best approach to measure nonlinearity and presents a more robust measurement technique that is more practically applicable. Finally the suitability of the approach and its limitations for monitoring are commented upon.
A Hybrid Data Driven Technique for Long Term Monitoring of Structures
Data interpretation is a challenging step for the monitoring of structures. Moving Principal Component Analysis (MPCA) has shown promising performance as a model free damage detection algorithm, which can be implemented for long-term monitoring of civil structures. Regardless of all the advantages associated with MPCA, it still has a main drawback which makes it less effective for long-term monitoring of critical structures. MPCA should be improved significantly in terms of time to detection. Therefore, the objective of this paper is to investigate and develop a new data interpretation approach based on principal component analysis but with less delay in detection. The efficiency of this innovative data interpretation approach (MPCASVM) is further investigated with the data from both lab study and a unique real-life structure. It has been observed from both lab and real-life study that MPCA-SVM outperforms MPCA in terms of time to detection.
Damage Inspection of Structures by Space-Wavenumber and TimeFrequency Analyses
This paper presents dynamics characterization and damage inspection of structures by using a boundary-effect evaluation method (BEEM) to perform spacewavenumber analysis of operational deflection shapes (ODSs) and a conjugate-pair decomposition (CPD) method to perform time-frequency analysis of dynamic responses of some structural points to a harmonic excitation. BEEM is for locating and estimating small structural damage by processing ODSs measured by a fullfield measurement system (e.g., a scanning laser vibrometer or a camera-based motion measurement system). BEEM is a nondestructive spatial-domain method based on area-by-area processing of ODSs and it works without using any structural model or historical data for comparison. CPD uses adaptive windowed regular harmonics and function orthogonality to perform time-frequency analysis of time traces by extracting time-localized regular and/or distorted harmonics. Both BEEM and CPD are methods for local spectral analysis based on local, adaptive curve fitting. Numerical simulations and experimental results show that the combination of BEEM and CPD for space-wavenumber and time-frequency analyses provides an accurate tool for damage inspection of thin-walled structures.
Non-Contact Nonlinear Ultrasound Scan of a CFRP Plate with Manufactured Damages
A Carbon Fibre Reinforced Plastic plate was manufactured with internal damages of different types. A Nonlinear Acoustic Wavemodulation technique was used to scan the plate. Non-contact ultrasound transmitters of own design were used as transducer, and a contact sensor was used to measure the wave in the composite. Scanning was made perpendicularly with the sensor being on the same side as the transducer. The technique can be adjusted for desired accuracy and speed.
Effect of Localized Plastic Deformation on Higher Harmonic Guided Wave Mode Generation in Plate
The effect of the evolving microstructure of the material in a plate, including localization of plastic deformation, is investigated with respect to generation of higher harmonic ultrasonic guided wave modes. Study of nonlinear ultrasonic guided waves indicates that a select number of primary wave modes will generate cumulative secondary modes sensitive to the microstructure of the material. Two such mode pairs – S1/S2 and SH1/S1 – are investigated with the help of nonlinear finite element analysis. The result of localized microstructure evolution is demonstrated for the S1/S2 mode pair. These findings will be useful for the detection of precursors to macroscale damage such as fatigue cracks.
Development of Scour Monitoring Methods Based on Scour Effect Analysis
Bridge scour is a major cause of bridge failures and is of significant concerns in the United States. Most previous studies focus on investigating the causes of scour but not on its consequences. Therefore, very few researches have been carried out on the response and feature changes of structures due to scour. The present paper mainly studied the scour effect on a single pile or pier. A theoretical solution was derived first to obtain the relationship between the scour depth and the pile response. Based on the observation, the present study proposed three possible methods for detecting and monitoring the bridge scour. Finally, a monitoring system using fiber optic sensors was designed and tested in the laboratory to verify the monitoring mechanisms.
Developmental Status of SHM Applications for Aircraft Structures Using Distributed Optical Fiber
This paper reports the current developmental status of our structural health monitoring (SHM) technology for aircraft structures. Our SHM technology is based on Brillouin optical correlation domain analysis (BOCDA), which is one of the distributed sensing technologies using optical fiber sensors. The authors have been developing, verifying and validating the BOCDA-SHM system. The airborne BOCDA-SHM device, which can monitor strain and temperature simultaneously, has already been manufactured so far, and sensor installation method adequate to aircrafts was developed. The durability to fatigue loads and environments of the device and sensors were also verified. This paper focuses two developmental tests from among developments previously mentioned. One is flight demonstration test conducted in order to verify the BOCDA-SHM system using the airborne BOCDA device and an attached optical fiber sensor. In the test, strain and temperature monitored by the system were agreed closely with those measured by strain gages and a thermocouple. The other is bearing damage detection test conducted in order to evaluate the probability of damage detection at bolted joint portion of carbon fiber reinforced plastics (CFRP). In the tests, micro-damages occurrences could be detected almost certainly by Brillouin spectrum shape changes even if damaged area did not reach the embedded optical fiber sensor.
Development Status of Optical Fiber Sensor Based Impact Damage Detection System for Composite Airframe Structures
Airframe structures, especially composite structures, are prone to damage by tool drops, bird strikes, hailstones, etc. The authors have performed the fundamental research for the Impact Damage Detection System (IDDS) based on optical fiber sensors since 1998. From 2006, more practical studies toward actual aircraft application were conducted with the University of Tokyo and Airbus through the JASTAC (the Japan Airbus SHM Technology for Aircraft Composite) joint research project by Airbus and SOKEIZAI center. This paper presents the recent achievement at KHI on enhancing the applicability of IDDS to actual aircraft, as well as the review of JASTAC joint research project achievement.
Damage Detection Technology for CFRP Structure Using MFC/FBG Hybrid Sensor System
We have been developing a Structural Health Monitoring (SHM) System for Carbon Fiber Reinforced Plastic (CFRP) structure of an aircraft. This system consists of piezoelectric transducer (PZT) actuators, fiber Bragg grating (FBG) optical fiber sensors and a control device. This SHM system can monitor debonding in interface between a skin panel and stringer flange by analysis of a Lamb wave which is a type of ultrasonic wave. Namely, this system is suitable to detect fatal damages for an aircraft structure in “Hot-Spotâ€. Development of this system will make it possible to reduce maintenance costs on airlines, to improve safety and reliability of aircraft, and in future, to achieve innovative aircraft structure which is lighter than present aircraft. We are conducting research and development with Airbus and SOKEIZAI center for practical applications of the SHM system for the near future. In this article, we will present a general outline of process and our achievement about JASTAC project with Airbus and SOKEIZAI center, and foresight of future works. Moreover, probability of detection (POD) assessment test will be reported in this paper.
Distributed Fiber-Optic Vibration Sensor for Gas Pipeline Leak Detection Using Hybrid Interferometer
In this paper, distributed optical fiber sensing system based on hybrid interferometer to measure and locate leaks for gas-filled pipelines. The phase signal induced by the gas leaky acoustic wave can be detected, and then the leakage position can also be calculated from null frequency of the output spectrum. The measuring principle and the leaky source locatiom method for the hybrid interferometer was analyzed. Localization performance was evaluated. The experiment was designed to evaluate leaks positions detection and location performance. The results show that the fiber optical sensor has good accuracy of the leaky positon, the fiber optic max inspection length is 10 kilometers, leaky hole is 2.5 mm, gas pressure range 0.5-1.5 MPa, the fiber optic sensor system location error is less than 1.68%.
Outline of the Japanese National Project on Structural Health Monitoring System for Aircraft Composite Structures and JASTAC Project
This paper presents outline of our structure health monitoring (SHM) project in Japan. We are developing four (4) SHM systems for composite structures of aircraft. This project is conducted by MHI (Mitsubishi Heavy Industries, Ltd.), KHI (Kawasaki Heavy Industries, Ltd.), FHI (Fuji Heavy Industries, Ltd.), MELCO (Mitsubishi Electric Corporation), Yokogawa (Yokogawa Electric Corporation), JAXA (Japan Aerospace Exploration Agency) and University of Tokyo. SOKEIZAI Center (former known as RIMCOF - Research Center of Advanced Metals and Composites) is managing them under METI (Ministry of Economy, Trade and Industry of Japan) contract. METI has founded this project. This paper also, describes JASTAC (Japan-Airbus SHM Technology for Aircraft Composite) project.
Fabrication and Test of a Low-Cost Intensity-Based Optical Fibre Accelerometer for Wind Turbine Blade Monitoring Application
The paper presents the fabrication and testing of a low-cost fibre optic accelerometer designed for wind turbine monitoring. The accelerometer was fabricated using plastic optical fibre enclosed in a plastic casing made from 3D printing. Nine fabricated accelerometers were mounted along a cantilever beam w a non-rotating wind turbine blade under wind excitation. An output-only non-model based approach namely NExT (Natural Excitation Technique) and ERA (Eigensystem Realization Algorithm) were chosen for modal analysis of the cantilever beam. The fibre optic accelerometers produced comparable time response, mode shape and damping ratio with the reference piezoelectric accelerometers in terms of amplitude and accuracy.
Evaluation of the SDDLV Method for Damage Detection on a Full-Scale Highway Sign Support Truss
Structural health monitoring (SHM) and damage detection have drawn increased attention in Civil Engineering community as our deteriorating infrastructure would benefit from monitoring systems to optimally allocate resources for repair and replacement. Effective and reliable SHM techniques have been investigated by many researchers for different types of civil infrastructures. In this study, a stochastic dynamic damage locating vector (SDDLV) method for damage detection on a fullscale highway sign support truss is evaluated. Different damage scenarios are created on the numerical model of this structure. The performance of SDDLV for damage detection on this type structure is discussed.
Ultrasensitive Optical-Fiber Ultrasonic Sensor Based on Phase-Shifted Fiber Bragg Gratings
Using optical method in structural health monitoring (SHM) is getting more and more attentions recently due to the inherent advantages in fiber optical method. In this research, we proposed an ultrasonic phase-shifted fiber Bragg grating (PS-FBG) sensor and demonstrated its application to ultrasonic SHM. Based on this sensor, two different demodulation systems were developed, named as cascaded PS-FBG sensing system and PS-FBG balanced sensing system, for acousto-ultrasonic detection and acoustic emission detection. The experimental results show that PS-FBG sensor with higher sensitivity compared to other existent FBG has a potential to replace traditional ultrasonic lead-zirconate-titanate sensors.
Identification of Structural Damage Based on a “Weakâ€ Formulation of Locally Perturbed Structural Vibration
The recently developed pseudo-excitation (PE) technique [1, 2] was proven capable of characterizing structural damage under steady vibration. Relying on partial differential equations that characterize the local dynamic equilibrium of the inspected structural components, the PE technique in its primary form, however, presents high susceptibility to measurement noise. In this study, the technique was improved by establishing a “weakâ€ formulation in which a multitude of damage indices, involving derivatives of the structural deflection from lower- to higher-order, as a result of different integration steps, were proposed. The multi-order derivatives in the “weakâ€ formulation provide flexibility for optimally selecting the weight functions, integration intervals and other measurement parameters, to create multi-detection strategies with different levels of precision and noise immunity capabilities. As a representative example, the continuous Gaussian smoothing (CGS) was demonstrated, revealing stronger noise immunity than original PE technique. The effectiveness of the CGS was then validated experimentally by detecting multi-cracks in an irregular beam-like structure, showing satisfactory detection accuracy which could not be achieved by the original PE technique.
Investigation of Shear Force Mechanism in Electro-Mechanical Impedance (EMI) Technique for Structural Health Monitoring
The piezoelectric patches have started to play an important role in continuous online health monitoring of structures in recent years because of their diminutive shape, cost effectiveness and easy installation process. The research for structural identification and force transfer phenomena has recently become an area of interest researchers working in PZT related structural health monitoring. The PZT patch allow the systems and structures to monitor their own structural integrity while in operation and throughout their life, and are useful not only to improve reliability but also to reduce maintenance and inspection cost of systems and structures specifically, in the electro-mechanical impedance (EMI) technique based SHM, the PZT patches are either embedded or bonded with adhesive to the host structure to be monitored. When bonded on the surface of the structure, the adhesive bond forms an interfacial layer of finite thickness between the patch and host structure. The performance (sensing/actuating) of piezo transducer depends upon the ability of the bond layer to transfer the stresses and strains between the active PZT patch and the host structures. The mechanical and geometrical properties of the adhesive bond layer change the overall performance of the PZT-structure interaction model. The main objective of this paper to review the shear lags models developed so far and discuss their effective applicability EMI technique.
Efficient Detection Methods on a Composite Plate with Interior Embedded Fiber Optic Sensors via Impact Test
Next-generation marine vessels are adopting composite materials for substructural elements, and due to a lack of knowledge about these materials’ failure modes, the need for monitoring them is substantial. In this study, a structural health monitoring system is implemented on composite plates with Fiber Bragg Grating strain sensors, internally embedded during material manufacturing, to monitor the effects of impact loads under ocean wave conditions. Taking advantage of the properties of FBG strain sensors, the composites plates were specifically designed to use this material to more easily examine the progression of impactinduced damage and delamination. The placement of the fiber optic sensors was done strategically in an asymmetric way to examine locations with the highest strain levels for multiple modes of vibrations, as determined by a finite element model of the composite plate. The failure scenarios were experimentally conducted on numerous plate specimens via dynamic impact simulation by dropping a hammer from various heights until fiber breakage (highly-localized damage). In each loading case, after each prescribed level of damage, the composite plate was subject to a vibration test. The main objective of the study is to examine the sensitivity of various feature extraction techniques from the FBG sensor data to both damage detection and damage localization. The first class of techniques compared involves basic signal statistics (mean and moments, peak statistics, higher-order moments). The second class involves modal property extraction, and the final class involves autoregressive models, which can be used both in a predictive and model-model comparative way. In all cases, comparisons will be made in a statistically-rigorous sense by employing receiver operating characteristics, since all tests were conducted in a supervised learning mode.
Damage Mode Analysis in Composite Structures Using Lamb Waves for SHM Purposes
Composites are becoming increasingly popular materials in airplanes, as they can provide high stiffness, high strength and reduce total mass. Damage modes in composite structures are difficult to be detected and may cause a catastrophic failure of structures. Structural Health Monitoring (SHM) offers an effective method to inspect the state of composite structures. However, complex damage modes are hard to be identified by SHM, and some damage modes need to be analysed by Lamb waves from physical testing with the aid of monitoring process of SHM. Identification of delamination damage in composites by using Lamb waves is presented in this paper. Characterization of the delamination damage is associated with propagation of guided elastic waves. The testing method is developed for quantitative analysis of delamination of composite plates. Analytical results were compared with experimental ones. Finally, damage analysis was used to combine with the quantification of damage in SHM. It is also important to make accurate assessment of structural state and provide information for further design of SHM system.
Understanding Probability of Detection (POD) in Structure Health Monitoring Systems
Important criteria to qualify an on-board Structural Health Monitoring (SHM) system for aircraft flight demonstrations and fleet-wide usage include both an ability to detect damage of a specific size via the creation of a ‘Probability of Detection’ (POD) curve as well as an estimate of the associated false alarm rate. The POD curve provides the best estimate of crack size with 90% POD and 95% confidence (a90/95) and a basis for setting inspection intervals as part of maintaining aircraft structure integrity. MIL-HDBK-1823A [1] contains technical information related to generating a POD curve and is the guide used to quantify the detection capability of NDI systems used as part of the US Air Force’s Aircraft Structural Integrity Program (ASIP). While the intended outputs of SHM and NDI systems are very often the same, there are differences in parameters that affect their performance and implementation hat may require special attention. This paper will discuss using MIL-HDBK-1823A [1] techniques in the context of SHM and will provide valuable insight about how the characteristics of collected and processed SHM data affect the formulation of that system’s POD curve. Additional information covered includes discussions on controlling factors to be considered while collecting SHM test data and a degradation model that directly relates statistical characteristics of the data to simple and intuitive degradation coefficients.
A Novel Approach for Structural Health Monitoring Using In Situ Thermoelastic Stress Analysis
The Australian Defence Science and Technology Organisation has developed a novel low-cost, rugged and compact thermoelastic stress analysis (TSA) system for non contact full-field stress analysis based on a microbolometer detector. As commercial microbolometer devices invariably reduce in size and cost, microbolometer-based TSA could have significant ramifications for aircraft structural integrity management, especially in the area of in situ structural health monitoring (SHM). An integrated full-field stress measurement capability in operational aircraft could continually monitor structural ‘hot spots’ providing both diagnostic and prognostic information to aid in efficient structural management. This paper provides an overview of the technology and outlines some of its potentially unique SHM capabilities through laboratory coupon studies and demonstrations on a full-scale fatigue test of a flight-critical aircraft structural component. These experimental studies illustrate key aspects of this capability which in turn point to possible future directions in its development and application. Although the case study focuses on an aircraft structure, the concept has potential application to a wide variety of different engineering assets across the aerospace, civil and maritime sectors.
Development and Validation of Acoustic Emission Structural Health Monitoring for Aerospace Structures
Acoustic Emission (AE) method has been shown to be able to detect damage growth in metals and composites. Verification and validation are necessary in order to enable the management of the risk of operation of engineered structures that are being monitored. This presentation consists of two main sections: first an experimental study of AE hardware and algorithms to detect and classify damage in a complex shaped carbon fiber reinforced polymer composite (CFRP) structure. Second, we discuss what a validation program should contain to satisfy regulatory authorities that this health monitoring approach can be used as a nondestructive testing technique, with associated probability of detection (POD) estimation.
Structural Health Monitoring Using Percolation Sensors—New User Cases from Operational Airliners and Chemical Plants
Materials that are prone to corrosion are normally protected from a wet environment, and this can be achieved by isolations, such as casings, coatings or seals. However, as a result of the intensive use of engineering structures, those isolations can be damaged and aqueous liquids arising from spillage, condensation or rain can enter those confined spaces. An interesting tool to detect the underlying structural damage of sealings would be offered by the detection of hidden harmful liquids by appropriate sensor networks. Percolation sensors for detecting harmful liquids can be considered as bioinspired sensors. Their specific performance is in analogy to the swelling of organic materials after the uptake of water. It is well-known that all living organisms require aqueous liquids for enabling all sorts of biological functions, and the regulation of the respective water content is therefore essential. In analogy with the interaction of water with biomaterial, the swelling of organic compounds can be used to develop a group of sensors that are proven to be useful in various SHM applications. For this purpose, different percolation sensors were developed, and since two years, three operational airliners from Lufthansa were equipped with percolation sensor networks (Boeing 737-500, 2 x Boeing 747-400) protecting the floor structure below the galley, the lavatory and the service doors areas. Moreover, a couple of percolation sensors were installed in the isolations of outdoor pipelines in chemical installations of BASF Antwerp. The results obtained are very promising in all cases, and they show that the presence of corrosive liquids is indicated in a very reliable way proving the presence of structural damage of casings, coatings and seals.
Feasibility Analyses of Carbon Nanotubes for the Design of a New Hair Flow Sensor
Finite element analyses were performed to determine the effectiveness of vertically aligned carbon nanotubes (CNTs) as a sensor material in developing a nanoscale flow sensor. Physical properties including varying length, diameter, and moment of inertia were tested to find a design that optimizes the bandwidth for sensing wind flow. It was found that increasing the length of the CNT decreased its eigenfrequency from 1010 Hz to 106 Hz which was still well beyond the frequency of wind (in the range of 103 Hz). Changing other physical properties proved to be less effective in optimizing the sensitivity of CNT. The observations provided can be used as reference for future endeavors in incorporating CNT into sensor technology.
Sensor Network Configuration Effect on Detection Sensitivity of an Acousto-Ultrasound-Based Active SHM System
An investigation was performed to assess the sensitivity of an acousto-ultrasound SHM system to changes in the configuration of the sensor network for the detection of fatigue cracks. Two different sensor networks were selected for the study on a riveted aluminum plate subjected to cyclic loads. The detection sensitivity of the system was quantified by the probability of detection (POD) approach that is used in traditional NDE but in this case modified for active SHM systems. The objective of the present work is to propose a POD analysis framework for reliable quantification of SHM systems. The proposed framework takes into account environmental and operating uncertainties, including varying temperature and loading conditions. The main concept is outlined and its operational applicability is addressed for hotspot damage detection monitoring. The issues of proper data collection, both experimental and model-based using numerical models, are discussed, while a model-based temperature compensation method is employed to treat the environmental uncertainties. In addition, the active sensing diagnostics based on ultrasonic guided waves and signal processing techniques are presented. Finally, the overview of the statistical POD analysis methodology is outlined and discussed. The effectiveness of the proposed framework is experimentally assessed via its application to an aluminum coupon, with two distinct sensor network configurations attached, under varying temperatures and loading conditions. The POD results with respect to increasing crack length are extracted and the SHM system’s reliability for damage quantification for both configurations are compared and discussed.
Optimization of Bio-Inspired Piezoelectric Composite Hair Sensor—Mechanical Impedance Matching
A number of animal sensing systems are based on hair sensor mechanism. This paper analyzes the responses of hair sensor using analytical model and coupled finite element analyses. The results from the analytical and numerical model are compared. The mechanical matching of hair sensor is found to have significant effect on sensor performance. Based on the analysis, optimal design is obtained and corresponding materials and geometries of the sensor components are recommended.
Quantitative Detection of Shallow Corrosion Damage by Targeted Use of the Dispersive Behavior of Guided Wave Modes
Pipelines in chemical and petrochemical processing plants are fixed at various locations through attachments and supports either on ground or on pipe bridges. Those locations are prone to accumulate moisture which may result in corrosive substances directly leading to corrosion and affecting the outer pipe wall. Those corroded locations are often difficult to be accessed for visual inspection and possibly also other means of inspection too. The same applies for gas and water supply lines, polls for roofs or signpost dug into the ground. Monitoring of corrosion along those tubes and polls is a must with respect to operational and general safety as well as for environmental reasons mainly imposed by public authorities. Targeted use of dispersive ultrasound wave modes offers the possibility to detect corrosion-related wall thickness reductions along the path of the ultrasonic wave considering ultrasonic transmission measurements. The effect measured is based on the fact that both phaseand group velocity of the ultrasonic wave are a dispersive mode that varies with wall thickness. Through analysis of phase position and / or travelling time of specific modes, information resulting from the wall thickness reduction along the acoustic wave path can be accessed. This is made possible through Electromagnetic Acoustic Transducers (EMATs) which allow a pure mode excitation of a guided ultrasonic wave to be generated such that this excitation as well as receiving configuration allows an exact evaluation of the phase position to be obtained. Furthermore, a targeted impression of a desired trace wavelength is possible. Due to the EMAT principle, generation and recording of an ultrasonic signal is done without direct contact with the specimen. Fraunhofer IZFP’s newly developed EMATs not only allow the variation of ultrasonic modes, but also a change in the trace wavelength. Hence, by analysis of the phase in different specific modes and locations on the component to be inspected, important information for the quantitative error determination can be found. Particularly, this approach can be used to detect shallow wall thickness changes, which generate no measurable reflection signals.
Scattering Measurements and In Situ Imaging with Sparse Guided Wave Arrays
Knowledge of how guided waves are expected to scatter from typical defects is useful for detection, localization, and characterization of damage. Structural health monitoring using sparse guided wave transducer arrays has been frequently proposed, but scattering information is rarely incorporated as part of the analysis methodology. If scattering characteristics are represented as a matrix indexed by incident angle and scattered angle, sparse array measurements essentially sample this matrix. Prior work by the authors has demonstrated a methodology for estimating the entire scattering matrix from these sampled angles. Here we improve this methodology and apply it to the generic defect geometry of a crack growing from a fastener hole, where a notch is used to simulate a crack. First, a controlled experiment is performed to obtain guided wave data for estimating scattering matrices for notches of different lengths. Second, a similar notch is introduced in a different specimen that is geometrically more complex and thus closer to being representative of an actual part. The scattered signals from the second specimen are computed and then analyzed via both delay-and-sum imaging and minimum variance imaging, utilizing the previously estimated scattering characteristics for damage detection, localization, and characterization.
Damage Identification for Composite Structures Using a CrossCorrelation Reverse-Time Migration Technique
This paper presents a cross-correlation reverse-time migration (CCRTM) technique to quantify and image damages in composite structures using flexural wave signals. A linear piezoelectric transducer array is surfaced-mounted on a composite plate to image a circular-shaped damage. The suitable frequency range for this method has been discussed prior to the testing. The forward wavefield excited by the actuator is formed by a finite difference (FD) method. The scattered wavefield is reversed in time and back-propagated using the same FD method to perform the migration as the backward wavefield. Cross-correlation values between the forward and backward wavefields are obtained for every grid on the plate to image the damage. It is demonstrated that this CCRTM can accurately locate and image multiple damages with improved resolution and higher efficiency.
Continuous Thickness Monitoring—Experience from the Field and Lessons Learned
Since 2005 the Imperial NDE group has been developing permanently installed ultrasonic sensors for wall thickness monitoring. A waveguide based sensor is particularly attractive as it isolates the test specimen from the transduction, signal processing and communication components of the sensor. For applications in industry the reliability and sensitivity to wall loss is particularly important. A corrosion monitoring system has been commercialised via Permasense Ltd. and several thousand sensors have been installed in petrochemical plants around the world. As a result ultrasonic measurement data is available at an unprecedented rate and repeatability. This paper will present lessons learned from application of this SHM system in the field and an analysis of the influence of the different parameters that influence the wall thickness trend analysis. While the paper focuses on the particular thickness gauging sensor that has been used the understanding gained has much wider applicability to other SHM problems.
Long Term Stability of Guided Wave Structural Health Monitoring Using Distributed Adhesively Bonded Piezoelectric Transducers
Guided wave SHM is a potential technique for structural health monitoring of plate like industrial structures. Previous studies have shown that it allows detection of small defects (1.5% reflection changes) in the presence of environmental changes. Most guided wave SHM systems use bonded piezoelectric sensors to excite and receive wave propagation in the structure a temperature and advanced subtraction algorithms that subtract signals and baselines from the “healthyâ€ structure are used to robustly discriminate damage from benign changes. This works well in laboratory environments for short periods of time. The long term stability of SHM using these techniques was investigated in this paper. Instrumented aluminium plates were exposed to thermal cycling in an environmental chamber while S0 mode signals were acquired. Analysis of the SHM data revealed drifts in baseline subtraction results corresponding to progressive reduction in defect sensitivity levels of the experimental setup. These drifts were correlated with increases in coherent variability and suspected to be due to variations in the adhesive properties of the bonds between the sensors and the structure. Finite element analysis and further experimental results confirmed that changes in adhesive properties were a likely cause of the degradation in monitoring capability with time.
Beamforming of Lamb Waves Using 2D Arrays: A Comparative Study
Structural health monitoring (SHM) of plate-like structures can be performed using 2D array of transducers. An optimal array topology as well as an efficient beamforming scheme becomes essential when designing a beamformer based on 2D arrays. In this paper we consider both aspects of the beamforming design process. We outline a consistent methodology including theoretical, numerical and experimental investigation of a diversity of 2D array topologies in application to the beamforming of LWs. We compare a number of symmetrical 2D topologies forming circular, star-like and square patterns. In the second part we propose and analyze multi-static beamformers with sparse transmission apertures and compare their performance with classical beamformer based on the mono-static phased array approach.
A Small, Light, and Low-Power Passive Node Sensor for SHM of Composite Panels
The aim of this work is to develop a miniaturized, self-contained and ultra-low power SHM system for impact detection and localization in composite panels. The system is based on a sparse array of piezoelectric (PZT) transducers along with the dedicated sensor node. Methodologically, when the stress guided waves generated by an impact cross a certain mV threshold at one PZT, signals are acquired by all sensors. Next, a dedicated signal processing that combines filtering and dispersion compensation algorithms estimates the wave time difference of arrival among the different sensors. The processing framework, embedded within the sensor node, is optimized for speed and power consumption. Finally, data are wirelessly transferred to a remote station, for further structural monitoring actions. The wireless communication technology allows long distance data transmission without wiring, simplifying the deployment of large multi-device SHM monitoring systems such as those based on mesh-type or star-type networks, whereas each node in the network is able to communicate with the central gateway. The system performances in terms impact localization accuracy and power consumption are tested on a composite Zstiffened panel of 1000x1000x3 mm.
Embedded Inductively Coupled Sensors for Composite SHM
The desire for long term regular condition information for structures is becoming ever greater and there are several approaches which typically rely on permanently attached sensors. This paper describes an approach, that of embedding a sensor and coupling to it inductively. This is positive in providing reliable repeatable measurements, with a completely passive on structure component. A model of the coupling is developed using linear circuit analysis and reducing the system to a 2 port network. This model is key as it allows a separation of the ultrasonic and electrical parts of the model. In doing this, each can be optimised separately to ensure the best overall performance of the system. This paper will go through the development of this model and show how its output is used to optimise the sensor design, providing insight into the relative importance of different effects. Having shown how the performance may be predicted an experimental validation of this is presented and a discussion of practical implementation issues is carried out. This includes how inspections can be carried out fast enough to be useful, and over what range a practical inspection may be made. Finally the developed sensor package is shown and its performance in isolation and when embedded in a composite material demonstrated. This performance is compared to what would be expected with a conventional wired transducer and predictions as to its sensitivity for SHM applications commented upon.
Guided Wave Scattering for In Situ Health Monitoring of Slotted Fuel Flow Vents in Integral Stiffeners
Fuel weep holes in aircraft wing risers are common fatigue critical locations for crack initiation. Whilst cracks that grow from the surface of the weep holes toward the wing skin can be easily inspected outside the aircraft by traditional ultrasonic and current state-of-the-art stress wave based SHM methodologies, cracks that grow away from the wing-skin on the blind side of the hole pose a much greater challenge, particularly if the hole is an elongated slot. The damage from elongated slots, or fuel flow vent holes, can be located entirely within the shadow region with respect to the straight non-diffracting rays from surface-mounted actuators. This paper investigates the diffraction and subsequent scattering of the fundamental symmetric Lamb, shear horizontal and edge-guided Rayleigh-type wave modes from representative subsurface and hard-to-inspect damage on slotted fuel flow vent holes. It is shown with 3D laser vibrometry and 3D finite element simulations that the edge-guided Rayleightype wave propagating along the free surface of the slot can play a key role in the detection and health monitoring of damage located entirely within the shadow zone.
Cognitive Structural Health Monitoring and Its Key Technology
Inspired by the latest technological advances in the field of radar, a novel concept of cognitive Structural Health Monitoring (SHM) is presented in this paper. Cognitive SHM refers to the SHM system that is capable of interacting intelligently with its environment by adapting both its transmit and receive functions based on contextual awareness and expert reasoning. With the concept of cognitive, a guided-wave SHM method is developed to demonstrate the advantage of cognitive SHM. By numerical simulation, the capability of the presented method for detecting the damage near the edge of the structure under varying ambient temperature is validated. Meanwhile, the ingredients of cognitive SHM as well as the future research steps are discussed.
Minimum Sensor Density for Quantitative Damage Imaging
Quantitative information regarding the size and severity of structural damage is required for structural integrity assessment. In the context of structural health monitoring using distributed sensors, several imaging algorithms have been developed recently. However, a large number of sensors is needed to meet the correct sampling requirements for accurate image reconstruction, the number being dictated by the size of the prescribed imaging domain. It is shown here that this requirement can be greatly reduced, based on an understanding of the mathematical structure and properties of the multistatic data matrix. The concepts of bandwidth and rank of the data matrix are defined and their relevance for the required number of sensors is discussed. An approximate translation theorem is proposed, and on that basis it is shown that a twopass imaging procedure can be implemented, in which the required number of sensors is dictated by the size of the damage, which is generally much smaller than the imaging domain. In particular, it is shown that, provided the insonifying wavelength is selected to be approximately equal to the damage size, one can obtain sufficiently accurate images for the purposes of safety prognostics by using only six or seven sensors. This considerably enhances the prospects for a practical implementation of in situ imaging of structural damage in plate-like structures, at least in the absence of geometrical complexities such as stiffeners or cut outs. The influence of such complexities is currently being investigated.
Monitoring Impact Online with a Miniaturized Device for Composite Structures
On-line impact monitoring is urgently needed in the area of composite structural health monitoring and management to ensure the safety of large-scale composite structures of aircraft as well as to reduce the cost and time of maintenance. Here, aiming at on-line applications, this research puts forward a new digital impact monitoring method. Different from traditional processing methods, the new one is achieved in a digital way by turning the outputs of PZT sensors directly into digital queues. Based on the digital queues obtained, impact occurring area and energy grade are achieved. Depending on this method, a PZT sensor array based impact monitor with advantages of miniaturized size, low weight and low energy consumption is developed which can meet the strict on-line application requests. Details of the new impact monitor design are presented in this paper. Meanwhile, an evaluation on a complex composite structure, has been conducted to verify the performance of the new digital impact monitor. This new monitor shows its potential for on-line structural PHM applications on aerospace vehicles.
Aluminum Nitride High Temperature Strain Sensors
Aluminum nitride is introduced as a promising material for high temperature resonating sensors. Two types of resonant strain sensors, double-ended tuning forks (DETFs) and triple-beam tuning forks (TBTFs), are discussed. The performance of the devices at elevated temperature up to 450 °C is investigated. By comparing the experimental results of TBTFs with and without a frame that can decouple substrate strains it is shown that the frequency shift upon heating is dominated by the coefficient of thermal expansion mismatch between the silicon substrate and the aluminum nitride device layer. It is demonstrated that these sensors can be employed with high accuracy for strain sensing at high temperature. A possible field of application for this technology is the structural health monitoring of industrial and building components exposed to harsh environment operating conditions or during catastrophes.
MEMS Piezoelectric Energy Harvesters for Harsh Environment Sensing
A microelectromechanical systems (MEMS) energy harvesting device aiming at powering wireless sensor nodes for structural health monitoring (SHM) in harsh environments is presented. For these types of wireless sensor networks (WSNs), the sensor modules are required to operate at elevated temperatures (> 250°C) with capabilities to resist harsh chemical conditions, where the use of battery-based power sources becomes challenging and in turn enormously increases their cost to a point no longer economically efficient. To address this issue, energy harvesting technology is adopted here to replace batteries and provide a sustainable power supply for sensor nodes towards maintenance-free harsh environment WSNs. In particular, the micromachined device presented in this work utilizes piezoelectric energy harvesting via a composite diaphragm structure composing of piezoelectric aluminum nitride (AlN) and silicon carbide (SiC), both exhibiting superior material properties against hostile conditions, to harvest energy from periodic pressure pulses. The fabricated AlN/SiC diaphragm energy harvester is characterized at elevated temperatures and shows reliable power generation at > 300°C. A dc output power up to 1.8 μW is demonstrated when connecting the harvester to a power conditioning circuit under 1.61-psi (rms) pressure pulses. The generated power level together with verified high temperature performance of AlN/SiC energy harvester, identifies its great potential in serving as energy sources for harsh environment sensors.
Optical MEMS Pressure Sensors for Geothermal Well Monitoring
The technology for enhanced geothermal systems (EGS), in which fractures connecting deep underground wells are deliberately formed through high pressure stimulation for energy generation, is projected to enormously expand the available reserves of geothermal energy in the U.S. EGS could provide up to 100,000 MWe within the U.S. by the next 50 years. Nevertheless, there are still many challenges to understand and develop this resource for low cost energy production. Pressure measurements, in particular, are important for determining the state of the fluid, i.e., liquid or steam, the fluid flow, and the effectiveness of the well stimulation. However, it has been especially difficult to accurately measure pressure at temperatures above ~200ºC. MEMS technology has been employed for many years for extremely accurate pressure measurements through electronic readout means. By substituting optical readout of a sensor at the end of a fiber optical cable, it is possible to employ these highly accurate sensors within the harsh environment of geothermal wells. A two year, DOE-funded project has developed MEMS-based sensors specifically for this purpose. A multidisciplinary team of researchers from GE, QOREX LLC, AFL Telecommunications and Sandia National Labs developed the MEMS optical sensor and readback electronics, fabricated a fiber optic cable for downhole testing, and has tested the sensor in a geothermal well.
Development of High Performance BS-PT Based Piezoelectric Transducer for Structural Health Monitoring of HighTemperature Polymer-Matrix Composite Structures
This paper focuses on the development of a new BS-PT based piezoelectric material system for high-temperature SHM applications. The new material system has a high thermal limit with superior piezoelectric responses compared with commercial PZT based and intrinsic BS-PT material systems [1] [2]. With this newly developed material system, our in-house fabricated piezoelectric transducers can remain high piezoelectric responses at high temperatures up to 350°C continuously for at least 10 hours. The transducer signals and the SHM capabilities of these high-temperature piezoelectric transducers at different temperatures were studied and demonstrated on metal plates and in polymer-matrix composites. The good thermal tolerance and the high piezoelectric coefficients of these newly developed transducers can be also observed when the transducers are integrated with structures.
Development and Verification of an Aerothermal Thermal Protection System Heat Shield Instrumentation Plug for Flight on Mars Science Laboratory
The National Aeronautics and Space Administration has constructed and overseen the installation of seven instrumented aerothermal plugs into the Mars Science Laboratory (MSL) heat shield, scheduled for launch in the fall of 2011. Since the MSL heat shield is predicted to experience significant recession from simultaneous turbulent and laminar heating during Mars reentry, development of the aerothermal plugs represents a dramatic commitment in the quantification of flight risk margins resulting from state-of-the-art thermal protection design methodology. The aerothermal plugs were developed and manufactured by NASA Ames Research Center as part of the MSL Entry, Descent, & Landing Instrumentation (MEDLI) project. Each MEDLI Integrated Sensor Plug (MISP) is composed of Phenolic Impregnated Carbon Ablator (PICA), a single isotherm following sensor called the Hollow aErothermal Ablation and Temperature sensor (HEAT), and four thermocouples. This paper will describe the basic design, specifications, and processes used to develop and qualify the plugs for flight to an acceptable level of risk for the flagship mission. Results from random vibration, thermal vacuum, and arc jet testing are also presented.
Health Monitoring of Composite Structures Using Carbon Nanotubes
The increased use of fiber-reinforced composites as compared to steel or aluminum in large-scale structures offers numerous potential benefits in terms of weight savings, corrosion resistance as well as the capability to tailor other structural and functional properties. These structures are often exposed to a variety of conditions, including impact, shock loading and extreme changes in temperature. The fracture of fiberreinforced composites is a complex process involving accumulation of micro-scale damage. Acellent Technologies and University of Delaware have jointly working to develop an integrated system of carbon nanotubes and piezoelectric sensors for health monitoring of composite structures under funding from AFOSR. The technologies are integrated into a hybrid structural health monitoring (SHM) system that can detect both microcracks and localized damage/delamination in composite structures. The hybrid system combines the unique sensing capabilities of carbon nanotubes along with those of a piezoelectric sensor network to sense the location, nature and extent of damage.
Structural Health Monitoring in Near-Space Environment, a High Altitude Balloon Test
The advent of commercial space travel has inspired engineers to rethink design and operation of space transportation systems. Structural health monitoring (SHM) is seen as a promising technology to reduce time to launch and operation costs with simultaneous improvements in the safety of commercial space vehicles. This technology, however, should be validated in realistic environments. In this contribution, exposure of sensors and measurement hardware to the near-space environment was considered and its effect on SHM procedures was investigated. The flight profile of the high-altitude balloon included 1 hour and 36 minutes of accent, 57 minutes of float at 102000 ft and approximately 30 minutes of decent. Three SHM technologies were tested: elastic wave propagation, electro-mechanical impedance, and strain/temperature wireless sensing. The elastic wave propagation experiment consisted of structural sound speed calculation, damage detection, and acoustic emission studies enabled by commercially available hardware. The test was successful, showing variation of sound speed at different stages of the flight and potential to detect structural damage in bolted joint connecting elements of the payload. Acoustic emission activity was also measured. In the second experiment, electro-mechanical impedance of active sensors was measured. However, due to launch delay, only on-the-ground data was collected. The third experiment explored wireless sensing in near-space environment using a commercially available hardware configuration for strain and temperature sensing. Sensor data was collected wirelessly during the high-altitude balloon flight. The study suggests potential of active diagnosis for continuous SHM of space vehicles and indicates specifics of using off-the-shelf sensor solutions in the near-space environment.
Hybrid Passive/Active Impact Detection & Localization for Aerospace Structures
This paper presents finding from a recent set of formal performance assessments studies, quantifying the ability of a hybrid passive/active SHM system to detect impact damage. The SHM system itself is an integrated device including an array of PZT sensors with a central PZT actuator, analog-to-digital converter, memory and FPGA. In passive mode, the system uses the central PZT element for triggering and the array of PZT sensors to detect and localize acoustic emissions (AE) propagating from the impact source. Subsequently, an active mode is triggered to capture the guided wave (GW) response of the resulting impact damage to a narrowband excitation measured from the same PZT array. In both cases, a phase-coherent beamforming approach is used to process the data. A single node was mounted to a 0.6 x 0.6 m square 2 mm thick aluminum plate with a row of 20 fasteneres, which was then subjected to 36 impact events of ~20 J. In addition, 6 tests were conducted to further predict the presence of a loosened fastener on the assembly. Results of the study indicated that the AE mode was extremely sensitive to small impacts even at a great distance with localization errors on the order of 25 mm. Similarly, while the GW mode errors were closer to 50 mm for localization for ~0.5 mm dents, the error for detection of loosened fasteners was less than 5 mm.
Health Monitoring of Aerospace Bolted Lap Joints Using Nonlinear Ultrasonic Spectroscopy: Theory and Experiments
This paper presents theoretical and experimental study of nonlinear ultrasonic spectroscopy method for health monitoring of aerospace bolted lap joints. In this study, the interrogating waves generated by a transmitter piezoelectric wafer active sensor (T-PWAS) propagate along the structure, interact with the lap joint contact surfaces, carry the information of bolt preload and are picked up by a receiver PWAS (RPWAS). The contact acoustic nonlinearity (CAN) introduced by the interaction between guided waves and contact surfaces serves as an index for the assessment of bolt tight/loose status. Contact finite element models (FEM) are built to simulate the contact behavior of the lap joint surfaces under ultrasonic wave cyclic loading. The relationship between bolt preload and the CAN is investigated. Experiments on an aerospace bolted lap joint are carried out to verify the FEM predictions. The nonlinearity from the electronic equipment (function generator, amplifier, etc.) is addressed. Scanning Doppler laser vibrometer is used to visualize the wave propagation and interaction with the lap joint, and the results are compared with FEM simulation. Nonlinear effects such as higher harmonics are observed from the FEM predictions and the experimental results. The paper finishes with conclusions and suggestions for future work.
Damage Detection in Large Scale Composite Rocket Structures Using Modal Acoustic Emission and Phased Array Sensing Techniques
Solid laminate and core stiffened polymer based composite structures are now being incorporated into space exploration vehicles. Care must be taken to insure the integrity of these structures through damage detection and health monitoring. Alliant Techsystems (ATK) and Digital Wave Corporation (DWC) are developing an automated, portable impact detection system for composite launch vehicle and rocket structures based on modal acoustic emission. Modal acoustic emission technology has been used successfully in detecting composite impact events and in locating those events on composite structures using conventional sensor coverage schemes. These schemes, however, are not practical for many large structures which can be as large as 10 meters in diameter. Conventional transducer configurations add additional complexity, cost, and weight. To reduce the footprint of the impact detection system sensor arrays based on phased array techniques are being developed. This technique is being investigated on both solid laminate composites and aluminum honeycomb sandwich composites. Methods are also being developed to reduce complexity by integrating wireless acquisition systems to each array. The system is designed to notify the responsible engineer when damage is detected by cell phone. This paper presents initial results which show that this new approach is capable of detecting impact events and accurately locating them with a significantly reduced sensor footprint and system complexity. This work was supported by AFRL.
NASA Prototype All Composite Tank Cryogenic Pressure Tests to Failure with Structural Health Monitoring
This paper will present results of pressurizing a 943 gallon all composite tank to failure filled with liquid nitrogen and using GN2 to pressurize the ullage. This test involved fairly limited sensor information consisting of pressure, surface temperature Thermocouples and strain gage information. A Health Monitoring Technology was also demonstrated utilizing the hammer impact modal method after some of the pressure steps. This tank failed at 330 psig after pressure steps of 140-330 psig This demonstration was a precursor for the KSC Planned Test till failure using a 450 gallon all-Composite Tank pressurized using gas in the tank filled with Liquid Nitrogen. Health monitoring technologies will be evaluated for effectiveness to detect insipient tank failure. This paper will report on the test plan, and initial results for no ullege pressure Liquid Nitrogen fills and 32 psig low pressures Hydrostat. The Test to Failure using LN2 is planned for summer of 2013 at NASA Marshall Space Flight Center (MSFC). The tank will be fully instrumented and access the ability of various Structural Health Monitoring Methods to predict incipient failure. The health monitoring partners include NASA MSFC, University of Central Florida (UCF) Department of Civil Engineering
Life Cycle Monitoring of Thick CFRP Pipes for Spacecraft Structures
CFRP pipes are key elements for truss structures supporting heavy optical telescopes in spacecrafts. CFRPs’ low coefficients of thermal expansion and high stiffness improve the instrument performance. In thick CFRP pipes, however, significant through-thickness tensile stress arises in curing and operation in a low temperature environment like in space, resulting in delamination failure. Though several systems are established to clarify the failure mechanism in composite pipes, the developed systems could not directly measure the through-thickness strain and could not detect signs of the damage. Hence, in this study we established a fiber-opticbased system for monitoring through-thickness strain throughout life-cycle. A fiber Bragg grating (FBG) sensor was embedded at the through-thickness center of a CFRP pipe and through-thickness strain change was measured using the birefringence effect of the sensor. The FBG sensor could continuously monitor strain development throughout the life-cycle (i.e., curing and simulated operation in a low temperature environment). Furthermore, the sensor successfully detected delamination from discontinuous strain change. Additionally, FEA and comparison with a plate specimen confirmed the validity of the measurement. This technique enables to avoid premature delamination failure, resulting in improvement of reliability of CFRP pipes.
Using Diffused Fields for Monitoring the Structural Health of Wind Turbine Blades
Advances in wind turbine technologies will include increasing their reliability in order to decrease their cost. One important aspect of reliability is the downtime events that prevent a wind turbine from generating power. Improved health monitoring systems for wind turbine blades can provide wind farm operators with more information about the state of the blade in order to reduce downtimes associated with costly field inspections and repairs. A potential system for structural health monitoring is studied using the diffused fields present in the wind turbine blades caused by the consistent, stochastic, and natural excitation generated during operation. The Green’s function can be reconstructed from the recorded signals of two passive sensors placed within the diffuse field. In the case at hand, the reconstructed coherent signal contains multimodal and dispersive waves. Here we demonstrate the method through experimental tests on a CX-100 wind turbine blade located at the UCSD Powell Structural Laboratories. Impulse and random excitation experiments were performed on a skin section of the blade in the max chord region. The results show a strong comparison between the measured Green’s function and the reconstructed Green’s function from the diffused field.
Validation of Vibro-Acoustic Modulation of Wind Turbine Blades for Structural Health Monitoring Using Operational Vibration as a Pumping Signal
This paper presents a crack detection technique for wind turbine blades using Vibro-Acoustic Modulation. The technique identifies nonlinear characteristics of a blade by utilizing the wind turbine operational vibration as a pumping signal. The theory underlying the technique is described and an experiment with a 900 watt smallscale wind turbine is conducted using a wind tunnel. Two other conventional VibroAcoustic Modulation tests were also performed with same blades to compare the results from these offline tests with the proposed online Vibro-Acoustic Modulation test using the turbine operational vibration as the pumping signal. It is shown that the structural vibration of the blades during operation lead to identical trends in the measured Vibro-Acoustic Modulation spectra for the cracked blade as the case in which the impact and piezoelectric actuator narrowband pumping signals are used.
Approaches to Acoustic Emission Monitoring with Applicability to Key Components in Wind Turbines
The operation of wind turbines is complex with possible failure mechanisms arising from a variety of structures and mechanical systems, such as the blades or gearboxes and drive systems. The scale of deployment of wind turbines continues to grow and they are deployed in increasingly remote and difficult to access areas; making their inspection and maintenance a challenging and costly activity. The use of effective structural health monitoring (SHM) systems can offer operators the potential to reduce down time and save costs. Acoustic emission (AE) is a sensitive and passive damage detection technique that can offer low power, continuous monitoring of structures and systems, as well as the potential to locate damage. As such it is well suited for use in SHM applications. In this paper the authors will present two distinctly different applications of AE to SHM, with strong applicability to wind turbine structures. Firstly a novel approach to source location is used to improve accuracy in anisotropic composite materials. Secondly, an approach for monitoring rotating machinery that utilises continuous AE wavestream data is presented, thus demonstrating that a single technology can be used to monitor differing elements of a turbine and allow the simplification of a global SHM system.
Virtual Assessment of Structural Health Monitoring Techniques for Wind Turbines Using Vibration Data
Operational Modal Analysis (OMA), also known as output-only modal analysis, allows identifying modal parameters only by using the response measurements of the structure in operational conditions when the input forces cannot be measured. This information can then be used to improve numerical models in order to monitor the operating and structural conditions of the system. This is a critical aspect both for condition monitoring and maintenance of large wind turbines, particularly in the off-shore sector where operation and maintenance represent a high percentage of total costs. The availability of commercial numerical aeroelastic simulation codes simulating the response of wind turbines in operation can be used as a virtual design and verification tool. Effects of design modifications and variations in the environmental and structural conditions can all be simulated using these tools. However, experimental test campaigns should be able to provide accurate and reliable data with which the model can be updated and be more representative of the real response. Thus, the improvement of these simulation models is strongly related to the improvement of the current Operational Modal Analysis (OMA) modal parameter estimation techniques. The main issue for these methods is that, due to blade rotation, force periodicity and the presence of control surfaces which modify continuously the system configuration, most of the applicability assumptions of OMA are violated. In this paper, some preliminary assessments on how to combine numerical and experimental techniques for Structural Health Monitoring (SHM) of wind turbines are investigated.
Cointegration for the Removal of Environmental and Operational Effects Using a Single Sensor
The cointegration algorithm has been developed recently as a powerful means of removing confounding influences from structural health monitoring (SHM) data. In this context, if one has two or more nonstationary time series, describing vibration data at a specific time period, one can cointegrate them in order to find a stationary linear combination of them. This means that it is essential to have datasets from different sensors in order to apply the specific technique, something not always feasible especially for on-line damage detection, due to the additional instrumentation costs and the amount of memory storage needed for these data. The current paper proposes a method that potentially allows cointegration to remove trends when a single sensor is present.
Composite Bonds Assessment Using EMI Technique
The research was focused on fibre reinforced polymer material. Its application has been increasing in some branches of industry. They are used for wind turbine blades or structural elements of new aircrafts. Moreover, there is an increasing trend to replace riveting of CFRP elements by joints using adhesive bonding. The crucial issue is to assess this bond quality. The performance of adhesive bonds depends on the physicochemical properties of the adhered surfaces. The weak bond can be caused by surface contamination of the adherend surfaces. The contamination leading to weak bonds may have various origin and be caused by moisture, release agent, hydraulic fluid, fuel, poor curing of adhesive and so on. In this research two different causes of possible weak bonds were selected for the investigation: 1. Weak bond due to release agent contamination, 2. Weak bond due to moisture contamination.
Structural Health Monitoring of Offshore Wind Turbines Using Automated Operational Modal Analysis
This paper shows the first results of a long term monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and resonant frequencies of the fundamental modes of the support structure. These parameters are crucial to minimize the O&M costs and to extend the lifetime of offshore wind turbine structures. For monopile foundations, scouring and reduction in foundation integrity over time are especially problematic because they reduce the fundamental structural resonance of the support structure, aligning that resonance frequency more closely to the lower frequencies. Since both the broadband wave energy and the rotating frequency of the turbine are contained in this low frequency band, the lower natural frequency can create resonant behavior increasing fatigue damage. Continuous monitoring of the effect of scour on the dynamics of the wind turbine will help to optimize the maintenance activities on the scour protection system. To allow proper continuous monitoring during operation, reliable state-of-the-art operational modal analysis techniques should be used. Those techniques are presented in this paper. The methods are also automated, so that the system can track the natural frequencies and damping ratios in a reliable manner.
Optimizing the Information Needed for Wind Turbine Health Monitoring
Offshore wind is a promising energy resource to face the climate change and to reduce the gas emissions. Over the last decade, this sector has experienced a very rapid growth, wind turbines have become much powerful and wind parks became bigger. However, the costs of operations and maintenance (O&M) still represent an important part of the cost of energy (about 30 %). Structural health monitoring (SHM) can play a major role to reduce these costs, by detecting coming failures before they occur and predicting the failure time. On a wind turbine (WT), hundreds of sensors can be found at different location and these sensors measure, for example, temperatures, output power, rotational speed, pitch and yaw angles, etc. Their primary functions are performance optimization and load control, but they are also used for condition monitoring. Unfortunately, a high number of sensors lead to more complexity and more sensor faults. As a result, relationships, correlations and redundancies are not used enough, even sometimes not used at all, in WT SHM. Moreover, through a high number of sensors, a wind turbine may be subjected to more and more frequent short stops that are not always necessary (false alarms). The main goal of this research is to optimize the sensor network and to improve its use for SHM. In this framework, several steps have to be considered. First, a theoretical analysis needs to be conducted to find what the minimum information is needed to understand a WT behavior. The WT behavior varies with respect to the environmental and operational conditions. Indeed, loads, rotation speed, power output, internal temperatures, etc. depend on these conditions. Assessing the health of a WT needs then to be performed in accordance to the working state of the WT. In the present research, the WT working states are described and classified into six main categories ('stopped', 'starting', 'running', 'transition around rated power', 'shutdown' and 'idling'). The possible previous and following states of each state have been determined, as well as the changes expected in the main signals. It is proven that a WT working state can be determined with only six sensors.
Structural Health Control—A Comprehensive Concept for Observation and Assessment of Damages Applied on a Darrieus Wind Turbine
This article presents an overarching concept, to observe and to assess structural damages, applied on a H-Darrieus wind turbine. This novel concept is more than Structural Health Monitoring (SHM) and is known as Structural Health Control (SHC). The SHC concept analyses the structure, identifies the damage and records the acting load as well as the usage parameters of the structure. Furthermore, it assesses the identified damages, followed by a damage prognosis and then proposes an optimal repair solution for the structure. All these processes are implemented in different modules in the SHC concept and are realized as a feedback control loop (see Fig.1). This feedback control loop ensures the stability of repaired structures. However, this comprehensive concept enables us to monitor and assess the damages of different structures, e.g. bridges, aircraft structures, automotive structures and wind turbines. Compared with the well-known horizontal wind turbines the idea of the SHC concept on a complex vertical wind turbine so called H-Darrieus wind turbine (see Fig. 3) is being presented. Based on analytical results, a Finite Element model is validated on analytical considerations and the subsequent structural analysis, which is embedded in the SHC, identifies the most critical areas on the turbine. Each of the critical areas can be an initiation of damage. Just these remaining areas have to be observed by the SHC concept on a H-Darrieus wind turbine structure. However, the novel SHC concept detects the damage in the critical regions of the H-Darrieus wind turbine, which were proposed from the Structural Analysis. Moreover, a procedure to record a load spectrum for vertical wind turbines is proposed.
A Study Investigating the Inter-Correlation of Wind Speed and Turbulence on the Accuracy of SCADA Based Wind Turbine Blade Load Reconstructions
The structural health of wind turbines is of great importance. Wind turbines are often sited at remote locations and repair jobs are only possible under certain weather conditions. If major parts are failing as for instance the gearbox or wind turbine blades not only weather and spare part availably needs to be considered, in addition are in general cranes or heavy lifting vessel needed. Wind turbines are equipped with a vast amount of sensors controlling the performance and power production of the turbine. Those sensors are not only driving the internal controller they are as well an essential part of the wind turbine reporting. The sensors provide the input parameters for the Supervisory Control and Data Acquisition (SCADA) System, a system transmitting in 10-minute intervals about 200 instances to the turbine operator. The SCADA system provides performance related attributes such as the generated power, the state of the turbine, the availability of the grid, a description of the wind field as well as control related attributes summarizing the activities of the yaw or pitch system [1]. A connection between the available operational data and the experienced loads, allows us to monitor the loads on the base of the SCADA data, data that is available for each and every commercial wind turbine. Load information can be used for advanced turbine control, to forecast incipient faults and to re-evaluate the remaining wind turbine lifetime. The relationship has a complex nature; wind turbine loads are driven by the inflow conditions such as turbulent, non-uniform or skewed inflow. In addition is the relation between wind and loading biased by the turbine control. Pervious research showed that it is possible to reconstruct the experienced loading if SCADA data is available [2]. The reconstruction process is based on statistics describing a time interval of 10-minutes. As for instance the wind field, which is here described by the average, max, min and standard deviation, calculated over a 10-minute interval. For load simulations is according to [3] is the assumption of a constant level of turbulence (per 10-minute bin) reasonable, at least for a wind speed ranging between 5 and 20 m/s. But how does the constant wind speed and turbulence intensity effect the reconstruction process, in particular for those wind regimes where the simplification is not reasonable? Here the data has been binned according to wind speed and turbulence intensity to test the impact of wind speed and turbulence intensity on the precision of the reconstruction.
Monitoring the Structural Integrity of Wind Turbine Blades Subcomponents Using Advanced Signal Processing Techniques
This paper presents a methodology, based on advanced signal processing, to monitor the damage evolution and to identify the structural failure modes in two composite I-beams. The composite I-beams with different geometrical configuration were subjected to cantilever tests. The I-beams represented the shear web-to-the spar caps connection of a wind rotor blade. Acoustic Emission technique was used to monitor the cantilever tests while Digital Image Correlation technique measured the displacement fields of the bond-lines and shear webs. The aim of the exercise was to investigate the influence of the geometry to the structural failure pattern, failure in the bond-line that connects the shear web to the spar caps and/or buckling of the shear web, and to monitor the failure process.
Structural Health Monitoring of Major Wind Turbine Components
Fraunhofer IZFP in Dresden is developing technologies for monitoring major components of wind turbines operating off- and onshore. Different structural health monitoring (SHM) techniques have been developed for monitoring rotor blades by acoustic methods, drive trains by operational modal analysis and welding seams in the foundation of offshore wind turbines by guided ultrasonic waves. Rotor blade monitoring technologies have been realized and tested at full scale under fatigue loading on a ground test bed and on a rotating wind turbine. The SHM-based techniques were compared to results obtained from conventional NDT techniques such as ultrasonics and thermography where validations have been made at damage critical locations of the different components considered. After completion of the full scale test selected damage critical parts were cut out of the rotor blade to allow for further inspection based on X-ray tomography and microwave technology. Acoustic emission monitoring results performed on an SHM basis showed comparatively good correlation when compared to the results obtained by conventional NDT. Drive train monitoring was realized through operational modal analysis. Generated through the excitation of the rotating blades the drive train started to resonate in its eigenfrequencies resulting from the eigenfrequencies of the drive train components such as bearings and gears. These oscillations caused by the different eigenmodes had to be determined through clear separation. Monitoring of foundation structures is a dedicated issue in offshore applications. In the range between low and high water levels welding seams become specifically prone to corrosion and cracking. In such cases a guided wave based monitoring system turns out to be valuable. Such a system is based on piezoelectric transducer arrays where the acoustic beam can be focused on the welding seam. This is currently realized after first tests have proven validity of the concept.
Ultrasonic Fields and Inspection of Huge Complex Composites
Reliability and effectiveness of widely used composites results in a significant demand for ultrasonic nondestructive testing. In this paper, an ultrasonic field calculating model is described for composite material. Focal distance, beam spread of transmitted ultrasonic field can be obtained by measuring transmitted ultrasonic filed. In order to guarantee more ultrasonic signals transmitted through composites, the sound axis of transducer should always keep consistence in normal plane of curved surface. A twin-robot testing system is set up to inspect the complex surface automatically by ultrasonic transmitted way. The experimental results above is effective and feasible for nondestructively ultrasonic testing huge complex composites.
Inspection and Monitoring of Wind Turbine Blade Embedded Defects During Fatigue Testing
The research presented in this paper focuses on a 9-meter CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between this blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This paper provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage.
Novel Method for Simulation of Lamb Wave Propagation Generated by an Interdigital Transducer
Recent years have brought great changes in the approach to the systems condition monitoring. Instead of using temporary attached sensors for scheduled technical inspection more and more often permanent health monitoring systems are mounted on structures. One of the techniques used for structural health monitoring systems is the Lamb waves-based technique. In this paper a novel method for modeling and simulation of complex shape transducers, used for the Lamb waves excitation and sensing, is presented and illustrated on the example of Interdigital Transducer (IDT). The proposed method is computationally efficient - it allows for an approximate prediction of the wave-field generated by an arbitrary-shaped transducer in a relatively short time.
Reduction of Model Complexity in Aerospace Structures with the Help of Approximated Stiffeners
Guided wave based systems are the most promising approach for Structural Health Monitoring of plate dominated components. A good understanding of wave propagation, which is required to design such a system, can be gained with numerical simulations. Models of complex structures still require enormous amounts of computational effort, despite the advancements in computing technology. This is due to the small wave lengths necessary for damage detection which in turn requires a fine discretization of geometry in standard Finite Element Methods. A lot of research groups focus on quite different approaches like spectral elements to overcome this problem, but none of these are ready for a wide use nor are they as flexible or wellproven as the conventional FEM. This paper will present ways to accelerate FEM simulation by reducing the model complexity.
Advanced Optimal Modal Reduction and Optimal Sensors Location Procedures for Structural Damage Identification
A two steps sub-structuring strategy is proposed for possible implementation in identification procedure. In the first step- the most important modes for a chosen master are defined with the aid of the OMR technique. In the second step – the optimal set of DoFs in the slave sub-structure is evaluated, with respect to the modal responses of the main sub-structure for the obtainable set of modes. Identification procedure, based on GA, demonstrate the efficiency of the proposed procedure by numerical example.
On the Use of Solitary Waves for the Prediction of Structural Buckling of Simple Structures
This paper presents the results of an experimental and numerical study on the use of highly nonlinear solitary waves (HNSW) for the detection of incipient buckling of slender beams. We use a chain of particles able to support the propagation of nonlinear solitary waves in contact with a beam subjected to thermal stress. The coupling mechanism between the solitary wave pulses and the beam is investigated numerically by means of a discrete particle model and experimentally. We find that certain characteristics such as amplitude and time-of-flight of the solitary waves are related to the raise in temperature of the structure, and therefore to the applied stress on the beam. In this paper we also discuss the challenges and the feasibility of the proposed HNSW-based health monitoring system to detect neutral temperature, i.e. the temperature at which the stress in the structure is zero.
Crack Pattern Quantification for Advanced Visual Inspection of Concrete Structures
Visual inspection (VI) is the most common non-destructive evaluation (NDE) technique for concrete structures. In many post-event assessments, cracks information including width and pattern are recorded and decision is made based on engineer experience and knowledge. However, residual closed cracks are sometimes the only available data for VI due to adjacent elastic members, earthquake displacement spectrum, or re-centering systems. Therefore, the measurements may lead to erroneous decisions. To overcome this problem, this paper proposes a novel damage index based upon Fractal Dimension (FD) analysis of residual cracks as a complementary method for VI. FD can temporally monitor a quantified crack pattern and facilitate interpretation of damage grade (DG). This algorithm was validated through experimental studies on a large scale reinforced concrete shear wall (RCSW). The results demonstrated improvement of visual inspection considering the new FD-based damage index.
Leakage of Guided Waves in Embedded Waveguides of Arbitrary Cross Section
A mathematical formulation is presented to compute the dispersion characteristics of waveguides with arbitrary shape and mechanical characteristics that are surrounded by infinite isotropic viscoelastic media. The propagation problem in the embedded waveguide is described via a Semi- Analytical Finite Element (SAFE) formulation, while a regularized 2.5D Boundary Element Method (BEM) is used to represent the radiation problem in the surrounding medium. The wave dispersive equation is obtained by imposing continuity and equilibrium conditions at the SAFE-BEM interface, resulting in a nonlinear eigenvalue problem. The generalized Snell-Descartes law is then enforced as additional constraint at the interface to determine the correct phase of the transverse wavenumbers. Complex poles relative to physical leaky and trapped guided modes are searched by means of a contour integral algorithm. Two cases are presented: a circular steel bar embedded in grout, for which results are available in literature, and a H-shaped steel beam embedded in soil.
Bragg Grating and BOTDR Fiber Optic Principles Applied for Real-Time Structural Monitoring—The MONICO Project
Structural engineering is a field of engineering dealing with the analysis and design of structures supporting or resisting various loads. Studies of structural engineering requirements, specifically for densely populated areas, have indicated the need for high safety standards particularly in structural monitoring having in mind latest shifts from construction costs to life cycle costs and lifetime performance including safety and use. Structural monitoring is now being supported by various emerging technologies especially in areas of high seismicity where structural monitoring can be regarded of particularly high importance. Optical sensing technologies have a lot to offer in the field of structural monitoring providing the basis for condition assessment before, during or after any event. Structural monitoring based on fiber-optic technologies provide real-time, wireless and remote deformation sensing capabilities making them ideal regarding safety of vulnerable tunnel crosssections or sections where very high standards of safety are required. Quick and reliable structural assessment can be enhanced with integrated software that collects and processes the data and assesses the structural reliability of the lining. Optical sensing technologies are most of the times supported by the above algorithms and are technologically progressing day by day. However it is quite important that the already existing and emerging deformation technologies are evaluated and optimised so that they alone, or in combination, can provide optimal performance in the structural sensing of structures and particularly tunnels. The MONICO EC Project has developed a prototype system consisting of an optimized fibre optics based deformation-sensing system for real-time measurement of deformations of reinforced concrete linings in tunnels and a Decision-Support-System (including user interface and decision suitable algorithms) that can drive proactive maintenance and earthquake risk management and can assess the structural reliability of the monitored lining under operating and seismic loads. Through this paper, the technologies, algorithms and supporting software components will be presented together with the results of the final large-scale tests of the MONICO project.
Structural Condition Assessment of Bridges: Past, Present, and Future—A Portuguese Perspective
Improved and more continuous condition assessment of bridges has been demanded by our society to better face challenges posed by aging civil infrastructure around the world. Indeed, the recent collapses of the Hintze Ribeiro Bridge that killed 59 people, in Portugal, and the I-35W Bridge over the Mississippi River that killed 13 people, in the United States, pointed out the need for the implementation of Bridge Management Systems (BMSs) with more reliable tools to avoid those events. Recent developments suggest that, in an effort to create more robust bridge management, in particular the improvement of the structural condition assessment, the concepts of Structural Health Monitoring (SHM) should be merged into the BMS, in a systematic way. Thus, in order to have a broad discussion on the past, present, and future of that topic, an International Seminar was held at the Catholic University of Portugal. This seminar intended to bring together bridge designers and owners, researchers, and students to discuss the current condition of the bridges in Portugal and to highlight shortcomings in current bridge inspection, condition assessment, and maintenance strategies. Therefore, this paper intends to summarize the highlights of the seminar, especially the main lessons from the past, the current needs and limitations, and some guidelines to improve the bridge management using SHM technology.
The Role of SHM in Infrastructure Management
In the paper, structural monitoring concepts are reviewed and the corresponding capabilities in providing reliable information on the structural conditions during the lifetime of the structure are summarized. Concepts like materials degradation, obsolescence, maintenance and life-cycle cost are discussed in relation to the benefits that the presence of a permanent structural health monitoring system from the early stages of construction can provide for efficient infrastructure management in an integrated approach.
Combined MEMS Acoustic Emission and Strain Sensors for On Chip Data Fusion
The complexity of the damage evolution in structures requires redundant measurements from different sensing methods in order to increase the reliability of the Structural Health Monitoring (SHM) outcome for real time damage detection. MicroElectro- Mechanical Systems (MEMS) allow designing multiple sensors on the same device due to the diversity in the materials and manufacturing methods. In this study, acoustic emission (AE) and strain sensors are combined on the same device. The passive nature of the AE method is ideal for real time damage detection and location triangulation for inaccessible areas on large scale structures. Unfortunately the AE method is highly influenced by background noise. The presence of the strain sensor on the same device can be utilized as trigger for AE sensing when the structure is loaded higher level, which is the expected initiation condition of damage. The MEMS AE sensors rely on the capacitance change of two electrodes made of doped polysilicon and nickel, and are manufactured using MetalMUMPs. The sensitivities of the MEMS AE sensors operating in the range of 50 kHz to 200 kHz are comparable with the similar frequency piezoelectric sensors. The MEMS strain sensors are based on the piezoresistive property of polysilicon, which nominally may lead to more than 70 times higher gauge factor (sensitivity) than conventional metal strain sensors. However, the substrate and insulation layers as well as any packaging and adhesive material reduce the strain level transferred to the sensing element which is compensated by the higher gauge factor. In this paper, the modeling, design and characterization of two sensor types are presented. The sensors are tested on an aluminum plate when the substrate is directly attached to the plate using an adhesive layer. The sensor responses to the simulated acoustic emission signals and the static strain through introducing deflection to the plate are compared with the conventional piezoelectric AE sensors and metal gauges. The obtained results illustrate the advantages of MEMS sensors over the conventional sensors.
Non-Contact Ultrasonic Guided Wave Inspection of Rails
The University of California at San Diego (UCSD), unđer a Feđeral Railroađ Ađministration (FRA) Office of Research anđ Development (R&D) grant, is đeveloping a system for high-speeđ anđ non-contact rail integrity evaluation. A prototype using an ultrasonic air-coupleđ guiđeđ wave signal generation anđ aircoupleđ signal đetection has been testeđ at the UCSD Rail Defect Farm. In ađđition to a real-time statistical analysis algorithm, the prototype uses a specializeđ filtering approach đue to the inherently poor signal-to-noise ratio of the air-coupleđ ultrasonic measurements in rail steel. The laboratory results inđicate that the prototype is able to đetect internal rail đefects with a high reliability. Extensions of the system are planneđ to ađđ rail surface characterization to the internal rail đefect đetection. In ađđition to the đescription of the prototype anđ test results, numerical analyses of ultrasonic guiđeđ wave propagation in rails have been performeđ using a ÅISA algorithm anđ some of these results are shown. The numerical analysis has helpeđ đesigning various aspects of the prototype for maximizing its sensitivity to đefects.
Hyperbolic Signature Extraction of Deeper Rebar Layer in Concrete Bridge Decks Using GPR
Bridge collapses were reported worldwide. More than a quarter of the bridges in the United States are obsolete and deficient. The evaluation and maintenance of concrete bridge decks has drawn significant attentions from the government and its people over the past decades. A non-destructive evaluation (NDE) technique, ground penetrating radar, is preferred for its high speed, which causes minimal traffic interruption. The evaluation is generally based on rebar reflections. The reflections from the first rebar layer are usually strong enough to evaluate either the concrete material above this layer or the rebar corrosion condition. However, in most cases, only one third of the deck can be evaluated this way. Deeper rebar reflections are usually too weak to be detected because of the high attenuation through long distance and the masking effect of the first layer. Further processing is needed to extract the information from the second rebar layer. In this paper, a migration, image enhancement, and demigration process is proposed. The main idea of the proposed approach is to enhance the rebar reflection intensity by increasing the signal to noise ratio without significantly distorting the radargram. Its effectiveness is validated by the simulation study discussed in this paper.
Air-Coupled Ultrasonic NDT of Solids Using a Focused Electrical Spark Source
This paper presents a non-contact acoustic spark source for NDT applications. The acoustic pressure wave generated by an electric spark is focused by an ellipsoidal reflector from the inner focus of the reflector to the far focus. The focused spark source has high pressure amplitude and broad bandwidth. In this study, the authors present experimental study of using the developed spark source for generation of stress waves in solids. Both the Rayleigh surface wave and zero-group-velocity Lamb modes were measured in a 250mm concrete slab and in a 100mm steel plate.
Multiple Cross Validated Sensing System for Damage Monitoring in Civil Structural Components
Structural Health Monitoring (SHM) often relies and will increasingly benefit from combination of sensing systems rather than a single sensing approach. The obvious advantage is the acquisition of heterogeneous information that can be combined to verify the presence of damage in structural components. The drawback is the accumulation of large amount of data that has to be processed, making the practical aspect of SHM often times complex. This paper evaluates the benefits and the challenges of fusing data recorded from multiple Non-destructive Evaluation (NDE) methods, including Acoustic Emission (AE), Digital Image Correlation (DIC) and Infrared Thermography (IR) when testing masonry components. The application of the presented sensing system is demonstrated on partially grouted concrete masonry shear walls that were fabricated and tested at Drexel University as part of NSF funded project. First, the use of the Digital Image Correlation method is validated on small scale assemblages of concrete masonry. Results shown clearly show that DIC provides useful surface full field strain maps that show "hot spots" in the regions where failure occus. A drawback of DIC is the need to record images at a constant rate in order to capture fast occurring events such as crack formation, which typically results in large amounts of stored data. To this aim, this paper shows how AE can be a great complement to DIC measurements. Conversely, although AE is sensitive to newly formed cracks as well as growing flaws, it is also affected by unwanted noise sources (environmental and operational) that are not related to damage. While advanced pattern recognition techniques have been proposed to separate damage related information from spurious AE events, their robustness and effectiveness are still not universally proven. In this context, DIC offers visual, full field information that can validate AE results, while DIC monitoring can be triggered by AE in situ data acquisition forming an intelligent SHM system. Finally, based on the available DIC and AE damage sensing capabilities, IR was used to during masonry testing, and local changes of temperature are compared with relevant mechanical and NDE information.
Feasibility for Energy Harvesting from Surface Bonded/ Embedded Piezo-Patches in Addition to Structural Health Monitoring
Energy harvesting and Structural Health Monitoring (SHM) are individually considered somewhat established fields of research in themselves. The main objective of this research is to explore same piezo sensor for SHM as well as energy harvesting on real life structures, which is a new concept in itself. Preliminary experiments have been carried out in the laboratory environment to measure the voltage and power generated by PZT patches in the normal axial strain mode. An analytical model has been developed based on Euler Bernoulli’s theory for a beam under harmonic vibrations using which voltage output across a Lead Zirconate Titanate (PZT) patch bonded on the beam is determined. Also, the losses associated with the PZT patch are included in the model and quantified. Shear lag effect is also incorporated with varying thickness of adhesive bond layer. The measured output power from a piezo sensor 10×10×0.3 mm has been found to be of the order of few microwatts.
Impact Sensing in Sandwich Structures Using Highly Nonlinear Solitary Waves
Identification of impact events in sandwich structures is extremely challenging, because wave propagation in them can be highly complicated due to the interplay between facesheet and core materials. This becomes more evident when sandwich structures are exposed to harsh mechanical and/or thermal environments, which prevent the installation of sensing suites on their external surfaces. Therefore, conventional structural health monitoring techniques based on guided Lamb waves and their triangulations can often yield inaccurate and unreliable diagnostic outcome. To overcome this challenge, a novel diagnostic approach is proposed based on highly nonlinear solitary waves. Solitary waves are compactly-supported energy packets, which are generated by a balance of nonlinear and dispersive effects in nonlinear media, such as granular crystals. In this study, we fabricate a sandwich structure with a thick core that embeds arrays of granular crystals. Upon the external impact on the facesheet, granular crystals transfer energy and momentum of Lamb waves to highly nonlinear solitary waves. The solitary waves are recorded in real-time using embedded piezoelectric discs positioned at the tip of granular crystals. Based on the measured time-of-flights and disintegrated mode shapes of the solitary waves, we identify the location of impact on sandwich structures. The proposed granular crystal-based sensing system has a potential as an effective impact monitoring tool for inspecting structural damages in critical areas of sandwich structures under harsh environments.
Acoustic Emission Source Localization in Anisotropic Structures Through Nonlinear Kalman Filtering Estimation
This article presents a structural health monitoring (SHM) method based on two well-known nonlinear Kalman filtering algorithms, Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) to estimate acoustic emission (AE) source location in anisotropic plate-like structures. To validate the proposed method experimental tests are carried out on a carbon-fiber reinforced polymer (CFRP) composite panel instrumented with a sparse array of eight piezoelectric transducers arranged in four clusters. AE sources are simulated using an instrumented piezoelectric impulse hammer. In order to compare the two algorithms two performance metrics are used: 1) accuracy of AE source localization and 2) computational time. The accuracy and computational efficiency of the proposed framework demonstrate its potential application for SHM of anisotropic plate-like structures.
Piezoelectric Rosettes for Acoustic Source Location in Composite Structures: Results from “Bluntâ€ Impact Tests
Over the past few years, the authors have investigated the technique of Piezoelectric Rosettes for localization of AE sources. The rosettes use the directivity behavior of rectangular piezo-composite patches to yield the principal strain angle of the incoming AE wave without the need for wave velocity information. The use of at least two rosettes allows one to pinpoint the AE source location. Because the rosette-based AE source location algorithm is not dependent on the wave velocity, the method lends itself to monitoring complex composite structures where the velocity can change with wave propagation direction (anisotropy) or along the wave propagation path (tapered and multilayered geometries). This paper presents the AE source location results from “bluntâ€ impact tests conducted on two large curved CFRP composite panels with co-cured stiffeners and bolted shear ties of overall dimensions of 1 m × 1 m and 1.93 m × 1.42 m, respectively. The results of these tests are of interest to follow the evolution of damage during the impact events.
Novel Approach for Acoustic Source Localization Using Spiral Sensing
This paper presents an approach for acoustic source localization in aerospace structures using spiral sensing. Impact signals are received by the passive sensors arranged on a plate in Theodorous Spiral (TS) pattern. Due to inherent characteristics of spiral it has varying radius with and thus it may possess sensitivity to different wave numbers based on sensor combinations in the spiral. Thus, frequency dependent directional sensitivity can be exploited. Different force (source) of impact with impact location can be characterized using a same set up. Optimization technique has been used to estimate the source location. In spiral design, the sensors are co-located to sense acoustic emission from any direction, because there are at least three sensors for each direction with varying triangular faces which improvises both directional and frequency sensitivity. Hence, impact location and impact type can be characterized more accurately. The performance of the spiral for source location is investigated on an aluminum plate.
Difficulties Associated with the Acoustic Source Localization in Anisotropic Plates and Its Solution
Acoustic source localization technique for anisotropic plates proposed by the authors in an earlier publication is applied here to a carbon fiber composite laminate. Difficulties associated with this technique are investigated. It is noted that the accuracy of this technique depends strongly on the accuracy of the measurement of the time delays in recorded signals by three receiving sensors placed in close proximity in the sensor cluster. The sensor cluster is needed to obtain the direction of the acoustic source without knowing the material properties of the plate. It is proposed here that to obtain the accurate time delays not the full time histories but only the beginning part of every time history (the first dip and peak) should be taken for the subsequent steps of signal processing. With these modifications improved predictions are obtained.
Near-Field 2D-MUSIC Algorithm Based Impact Localization Using Linear Array
This research presents a near-field impact monitoring method based on the 2-D multiple signal classification method for the composite structure. Since elastic waves attenuate quickly in the composite structures and impacts may happen near the sensors, near-field impact monitoring method is important. However, most of the methods reported base on the far-field assumption. When impacts occurring not far enough from the sensors, this belongs to the near-field situation, where the elastic wave fronts are spherical and the plane wave hypothesis is no longer valid. To verify the proposed method, the experiment is preformed on composite plate structures. The localization results are in good agreement with the actual impact positions
A Sequential Statistical Time Series Framework for Vibration Based Structural Health Monitoring
The goal of this study is the introduction and experimental assessment of a Sequential Probability Ratio Test (SPRT) framework for vibration based Structural Health Monitoring (SHM). This employs the residual sequences obtained using a single stochastic time series model of the healthy structure and is based on a combination of binary and multihypothesis versions of the SPRT. The framework’s performance is predetermined via the use of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline experiments, while it requires on average a minimum number of samples in order to reach a decision compared to Fixed Sample Size (FSS) most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.
Probabilistic Damage Detection in Beam Structures Based on the Neutral Axis
The centroid of stiffness is a universal parameter and its position in a cross-section can be evaluated for any load-carrying beam structure as the position of the neutral axis under conveniently chosen loads. Thus, a change in the position of the neutral axis within a cross-section can indicate change in the position of the centroid of stiffness, i.e., unusual structural behaviors. This study presents a probabilistic damage detection method based on changes in the position of the neutral axis. Therefore, the method presented in this paper is potentially applicable to a large variety of beam-like structures. Dynamic strain data from the US202/NJ23 highway overpass in Wayne, NJ, is used to assess and validate its performance. The results show that the neutral axis location is varying even when damage is not present. Reasons to this variation are proposed and the accuracy in the evaluation assessed. The conclusion of this study is that the position of the neutral axis can be evaluated with sufficient accuracy using dynamic strain measurements and presents its potential to be used as a damage sensitive feature.
On the Impact of Prior Engineering Perception on Structural Health Diagnosis: Analysis of a Case Study
We illustrate an application of Bayesian logic to analysis of monitoring data and to inference of structural condition. The case study is a 260 m cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner installed a monitoring system that includes fiber-optic sensors. These sensors are FBG-based and allow measurement of changes in deformation with an accuracy of the order of just a few microstrains, with respect to the value at installation. After one year of system operation, which included maintenance on the interrogation unit, the data analysis showed an apparent contraction of the cable lengths. This result is in contrast with the expected behavior, which should be of elongation, due to creep, shrinkage and relaxation. We analyze how a rational agent makes sense of the observed response, and particularly we discuss to what extent he/she is prone to accept the sensor response as the result of the real mechanical behavior of the bridge versus a mere malfunction of the interrogation unit. In this exercise we consider four psychological profiles, which vary based on their personal trust in the reliability of the instrumentation and on their knowledge of the structural behavior of the bridge. Using Bayesian logic as a tool to combine prior belief with sensor data, we highlight how the extent of prior knowledge can alter the final engineering perception of the current state of the bridge.
Dynamic Bayesian Network for Operational Modal Identification
Application of the dynamic Bayesian network (DBN) for modal identification under operational conditions is investigated. Under the assumption of Gaussian white noise, the stochastic state space model is converted to a DBN. Time-domain maximum likelihood estimation (MLE) is applied to learn the DBN. The expectationmaximization (EM) algorithm, initialized by parameters identified by the referencebased, data-driven stochastic subspace identification (SSI/DATA) method, is used to iteratively solve for the MLE. A numerical simulation example demonstrates the performance of the DBN-based identification method.
Nonlinear Model-Data Fusion for Post-Earthquake Assessment of Structures
This paper proposes a real-time methodology estimate earthquake induced structural damage in instrumented buildings. The methodology relies on model-based state estimation to perform the assessment throughout the complete structure. In a similar fashion to existing data assimilation methods for nonlinear systems, the proposed observer uses sparse noise contaminated measurements of structural response and a model in order to perform the estimation. In contrast with existing methods, such as the extended and ensemble Kalman filters, the proposed observer does not require model linearization or Monte Carlo simulations. It is shown by means of stochastic simulation in a ten-story nonlinear building structure that the proposed model-based observer outperforms the extended Kalman filter and provides similar results to the ensemble Kalman but with the advantage of requiring only a small fraction of the computational time.
Quantification of Pavement Condition by Tire/Road Noise Measurement
A method is developed to assess the pavement condition, using measurements from a microphone mounted behind the tire of a moving vehicle. Such measurements will include tire/road noise that carries information about the road feature. The proposed method uses standard deviation (STD) of the amplitudes of frequency spectra from the acoustic measurement to assess the road condition while the vehicle is moving. During the driving, the change of road condition will excite the vehicle resonance varying with frequency, which will lead to the pressure varying with frequency. Hence the standard deviation of the frequency content of the tire/road noise can be used for pavement condition assessment. The analysis begins with acoustic pressure measurements being made over various road surface conditions. The road is divided into continuous sections of equal length. For each road section, Fourier Transforms are taken over various time windows and averaged and a standard deviation of the average frequency spectrum is computed. This yields a set of STDs representing the surface conditions of each road section. According to the amplitudes in the frequency spectra of the collected acoustic measurement the frequency range of concern is from DC to 2 kHz. A linear regression analysis is performed between the known pavement condition index (PCI) and the average STD of the road. The overall pavement condition is quantified by this resulting equation. Successful applications of this method are demonstrated by a high negative linear correlation with a coefficient of - 0.77 between the average STD for a specific road and its corresponding PCI. A linear regression equation is obtained to calculate PCI through the average STD for each road. The results indicate that the frequency spectrum of acoustic measurement could be used for pavement condition assessment through the statistical analysis in real time.
Probabilistic Learning and Planning for Optimal Management of Wind Farms
Wind energy is a renewable energy source that has been growing rapidly in recent years. However, wind farms have relatively high cost, of which operation and maintenance (O&M) cost takes up 25-35%. An optimal strategy for O&M can significantly reduce this cost. Wind turbines are subject to fatigue-induced degradation and need periodic inspections and repairs. A maintenance strategy for the farm has to be based on the prior knowledge on the degradation process, and on data collected in-field, by sensors and visual inspections. Traditional methods to model the O&M process, as Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), have limitations which do not permit them to include properly the knowledge available, and may result in non-optimal solutions. Specifically, the conditional probabilities for the evolution of the degradation state and the precision of the observations are usually affected by epistemic uncertainty. While MPDs and POMDPs are formulated for fixed transition and observation probabilities, the Bayes-Adaptive POMDP (BA-POMDP) framework treats those conditional probabilities as random variables, and is therefore suitable for including epistemic uncertainty. In this paper we propose a novel method to learn and update the distribution of these variables during the management process; and to select the optimal strategy under model uncertainty. We validate our approach with synthetic data, and we show that the management cost of a wind farm can be significantly less than that related to the POMDP approach, as the BA-POMDP framework allows finding robust optimal solutions, and learning from the environment to adapt the policy depending on the observations collected.
Reliability-Oriented Optimization of Replacement Strategies for Monitored Composite Panels for Aircraft Structures
This paper proposes a reliability-based optimization of the life-cycle cost of smart aircraft components in the framework of Bayesian damage update methodology. Statistical models of the structural health monitoring, such as the probability of detection and probability of false alarm prove to be essential for finding the optimal inspection and replacement strategy.
Reliability-Based Condition Assessment of Bridge Deck Using Long-Term Monitoring Data
We propose a reliability-based approach for progressive assessment of structural condition of bridge deck sections, and apply the proposed method to the suspension Tsing Ma Bridge with the use of long-term strain monitoring data. After performing wavelet multi-resolution analysis to extract the live-load-caused strain components from raw measurement data, the strain sequences acquired from different structural members on the same deck section are synthesized to derive the dynamic internal forces (axial force, bending moment, and shear force) of the monitored deck section. The Weibull mixture modeling is then carried out to characterize the probability density distribution of the peaks of each internal force component, and the corresponding reliability index is obtained. The reliability index of the deck section is evaluated by considering the different internal force components in series connection. The evaluation result of reliability index at deck section level is obtained year by year with cumulated strain monitoring data, thus tracking the variation in structural condition of the bridge deck evolutionarily.
Understanding Environmental Effect on Building Vibration for Structural Health Monitoring Using Event Detection and Causal Analysis
Structural damage diagnosis algorithms often involve analyzing vibration responses to extract information about dynamic characteristics closely related to integrity. The vibration data are, however, often influenced by various environmental effects, which may degrade diagnosis performance and result in erroneous decisions. This paper focuses on understanding the consequence of significant environmental effects, such as trains passing nearby, on building vibration data using event detection methods and causal analysis. The event detection methods identify significant events using wavelet analysis, which is effective for decomposing non-stationary signals. The causal analysis allows us to investigate wave propagation patterns in structures by quantifying causal dependencies between measurements collected from different locations using directed information. These methods are applied to acceleration data collected from 40 accelerometers deployed to an 11-story office building located next to railway. The results show clear patterns of causal dependencies among vibration data from different locations in the building, and their patterns change under different environmental conditions. It provides insights about environmental loading effects on building vibration that can be helpful for improving the accuracy of damage diagnosis
An Approach to Fault Detection Using a Unified Linear Gaussian Framework
Presented in this paper is an approach to fault diagnosis based on a unifying review of linear Gaussian models. The unifying review draws together different algorithms such as PCA, factor analysis, hidden Markov models, Gaussian mixture models and linear dynamical systems as simple variations of a single linear Gaussian model. The focus in this work is on PCA, factor analysis and linear dynamical systems in order to compare static and dynamic data models. One of the advantages of using a unified framework for these already well known models is the quantification of uncertainty, and the ability to evaluate the probability of a dataset given a model. To highlight the applicability of such an approach to a fault diagnosis application and to establish in some way its robustness to environmental variability, a set of data collected from the suspension system of a ground vehicle is used.
Automatic Damage Classification Based on Wave Cluster and Principal Component Analysis
Principal Component Analysis (PCA) plays a significant role in SHM field. There are plenty of algorithms that use PCA either directly or indirectly to detect damages in structures. Although PCA shows a successful role in damage detection but it still needs a complimentary step for automatic damage classification. It means a human effort still is required to classify different clusters that exists. Among different clas- sifiers, the wavelet classifier posses many dedicated merits. This work concentrates on automatic classification of damages with different severities. To do this, PCA is used as a tool for dimensionality reduction and then a wavelet classifier is applied on the result to classify different patterns in the structure each of which associated to significant state of the structure. This work involves experiments with composite plates powered by piezoelectric transducers as sensors and actuators. Damages are introduced into the structure as mass with different weights.
Energy Harvesting and Power-Management for Sensing Skins in SHM Applications
Self-powered operation can substantially ease the deployment and adoption of SHM systems. In this work, thin-film power-management systems are presented, which achieve energy harvesting and wireless power delivery to sensing subsystems all using the same thin-film amorphous-silicon (a-Si) technology. This enables a future path to systems with low assembly complexity. Two thin-film inverter topologies for wireless power delivery are discussed: (1) a class-D switching power inverter; and (2) an LC-oscillator-based power inverter. Among these, the class-D power inverter achieves wireless power delivery of 120 μW at an efficiency of 22%, while LCoscillator- based power inverter achieves wireless power delivery of 22 mW at an efficiency of 31%. Though the power levels are adequate for some SHM systems being envisioned, the superior power level of the LC oscillator makes it relevant for a wide range of systems.
Patterned Carbon Nanotube Sensing Skins for Strain Sensing
Increasing demand for structural health monitoring (SHM) systems has accelerated technological innovation in a number of engineering arenas including in the development of new sensing technologies. Multifunctional materials that exhibit an ability to self-sense their response to external stimulus has shown promise for future SHM applications. One specific multifunctional material previously explored for SHM includes carbon nanotube (CNT)-polymer composite thin films deployed as sensing skins. While CNT-based sensing skins have shown great potential, their fabrication through layer-by-layer deposition has prevented fine geometric control of the skin features. The lithographic techniques commonly used in microelectromechanical systems (MEMS) could improve utilization of CNT-polymer sensing skins by allowing such films to be patterned into any desired geometric configuration. This study investigates the influence of geometric shape on the mechanical-electrical sensitivity of patterned CNT-polymer composite thin films. Layer-by-layer assembled single walled carbon nanotube (SWNT)-polymer thin films are patterned to varying sizes using a controlled lift-off process involving optically exposed sacrificial photoresist layers. Patterning quality is characterized using optical and scanning electron microscopy. The mechanical-electrical behavior of the various thin film geometries are analyzed through cyclic tensile load testing.
Towards Sensing Sheets Based on Large Area Electronics
The need for reliable and inexpensive Structural Health Monitoring (SHM) of civil structures and infrastructure is rapidly increasing. However, today’s SHM technologies are based on sparsely-spaced sensors that do not allow reliable early detection of anomalies at locations of even modest distance from the sensors. This form of indirect damage detection raises the need for complex localization and sensing algorithms to overcome the limited resolution. The hypothesis of the presented research is that by substantially increasing the sensor response to local anomalies (i.e., increasing spatial resolution of sensors to a centimeter-scale over several square meters), direct sensing leads to improved reliability in damage detection. Technologies based on Large Area Electronics (LAE) can enable direct sensing and can be scaled to the level required for SHM. Thin-film strain gauges and control circuits are integrated on the flexible electronics and deposited on a polyimide sheet that can cover large areas. At the present stage of this research, crack tests with strain gauge sensors are used to study the layout of sensing elements on the LAE sensing sheet. Additionally, a characteristic identification test is carried out to evaluate alternative designs for sensing elements based on thin film transistors. The general concept of sensing sheets based on LAE (see Figure 1) and the results of both tests are presented in this paper, demonstrating the feasibility and benefits of LAE and the direct sensing approach for damage detection over large areas of structures.
Sensing Skin for Condition Assessment of Civil Structures
The technology facilitating condition assessment of civil structures is currently limited. This is mainly due to the lack of scalability of existing sensing solutions, as well as the difficulty to link sensor signals to prognosis. With recent advances in conducting polymers, it is now possible to deploy network of flexible sensors over large areas, at low cost. Here, we propose to use a novel sensing skin that consists of a network of soft elastomeric capacitors (SECs) deployed in an array form. Each SEC acts as a surface strain gauge transducing local strain into changes in capacitance. Using strain data facilitates the feature extraction task for structural prognosis. We demonstrate the capability of the technology at conduction condition assessment by extracting deflection shapes on a specimen equipped with the SEC network. Results show that the sensing strategy compares well against off-the-shelf strain gauges, and that it can be used to extract useful signal features.
The Use of Localized Sensor Networks for Damage Detection: A Likelihood Ratio Test Control Chart
This study presents the implementation of an output only damage detection method on an experimental structure by applying a mulitvariate change point analysis approach. The multivariate method, the Normalized Likelihood Ratio Test, is utilized to create control charts in order to localize damage on a small scale steel girder. Data from a dense sensor network is utilized to correlate a location on the frame with damage. 1. INTRODUCTION