Damage Detection Methodology Research Articles

Assessment of delay-and-sum algorithms for damage detection in aluminium and composite plates

Piezoelectric sensors are increasingly being used in active structural health monitoring, due to their durability, light weight and low power consumption. In the present work damage detection and characterization methodologies based on Lamb waves have been evaluated for aircraft panels. The applicability of various proposed delay-and-sum algorithms on isotropic and composite stiffened panels have been investigated, both numerically and experimentally. A numerical model for ultrasonic wave propagation in composite laminates is proposed and compared to signals recorded from experiments. A modified delay-and-sum algorithm is then proposed for detecting impact damage in composite plates with and without a stiffener which is shown to capture and localize damage with only four transducers.

Structural health monitoring with statistical methods during progressive damage test of S101 Bridge

For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Since structures are subject to unknown ambient excitation in operation conditions, all estimates from the finite data measurements are of statistical nature. The intrinsic uncertainty due to finite data length, colored noise, non-stationary excitations, model order reduction or other operational influences needs to be considered for robust and automated structural health monitoring methods. In this paper, two subspace-based methods are considered that take these statistical uncertainties into account, first modal parameter and their confidence interval estimation for a direct comparison of the structural states, and second a statistical null space based damage detection test that completely avoids the identification step. The performance of both methods is evaluated on a large scale progressive damage test of a prestressed concrete road bridge, the S101 Bridge in Austria. In an on-site test, ambient vibration data of the S101 Bridge was recorded while different damage scenarios were introduced on the bridge as a benchmark for damage identification. It is shown that the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.

A multi-reference-based mode selection approach for the implementation of NExT-ERA in modal-based damage detection

A modal-based global method for identification, localization and quantification of damage of a shear frame structure is presented in this paper. The method involves identification of modal parameters from the vibration responses of the structure only (output only) using a combination of natural excitation technique (NExT) and eigensystem realization algorithm (ERA), and determination of stiffness parameters using very few identified modes by employing a least squares solution procedure. Finally, damage is indicated by comparing the stiffnesses of the (possibly) damaged structure with that of the undamaged structure. The implementation of NExT-ERA requires a reference channel for modal identification, but several limitations of using a single reference channel have been identified from the investigations of this study. To overcome the limitations, a new approach called multi-reference-based mode selection is proposed in this paper. The approach makes use of all the available DOFs, but one at a time, as reference channels and identifies the best representative modes of the structure by a mode screening process and a mode separation process. The approach is also automated for expediency and possible real-time application. The presented damage detection methodology is implemented on the analytical phase I and experimental phase II of the IASC-ASCE SHM benchmark problem and shows an accuracy of around 90% in damage detection. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.

Charged System Search Algorithm Utilized for Structural Damage Detection

This paper presents damage detection and assessment methodology based on the changes in dynamic parameters of a structural system. The method is applied at an element level using a finite element model. According to continuum damage mechanics, damage is represented by a reduction factor of the element stiffness. A recently developed metaheuristic optimization algorithm known as the charged system search (CSS) is utilized for locating and quantifying the damaged areas of the structure. In order to demonstrate the abilities of this method, three examples are included comprising of a 10-elements cantilever beam, a Bowstring plane truss, and a 39-element three-story three-bay plane frame. The possible damage types in structures by considering several damage scenarios and using incomplete modal data are modeled. Finally, results are obtained from the CSS algorithm by detecting damage in these structures and compared to the results of the PSOPC algorithm. In addition, the effect of noise is shown in the results of the CSS algorithm by suitable diagrams. As is illustrated, this method has acceptable results in the structural detection damage with low computational time.

A Summary Review of Correlations between Temperatures and Vibration Properties of Long-Span Bridges

The shift of modal parameters induced by temperature fluctuation may mask the changes of vibration properties caused by structural damage and result in false structural condition identification. Thoroughly understanding the temperature effects on vibration properties of long-span bridges becomes an especially important issue before vibration-based damage detection methodologies are applied in real bridges. This paper presents an overview of current research activities and developments in the field of correlations between temperatures and vibration properties of long-span bridges. The theoretical derivation methods using classical structural dynamics and closed-form formulations are first briefly introduced. Then the trend analysis methods that are intended to extract the degree of variability in vibration property under temperature variation for different bridges by numerical analysis, laboratory test, or field monitoring are reviewed in detail. Following that, the development of quantitative models to quantify the temperature influence on vibration properties is discussed including the linear model, nonlinear model, and learning model. Finally, some promising research efforts for promoting the study of correlations between temperatures and vibration properties of long-span bridges are suggested.

Damage assessment of carbon fiber reinforced composites under accelerated aging and validation via stochastic model-based analysis

Composite materials used in technically advanced structures are subjected to constant aging from exposure to changing environmental conditions. Studying the effects on such materials due to exposure to varying temperature, humidity, ultraviolet radiation, etc. reveals the impact on their mechanical behavior. This study assesses alterations in static, dynamic, and viscoelastic response of polymer matrix woven carbon fiber lamina composites upon exposure to varying environmental conditions recreated in a climatic chamber. Therein, specimens suffered temperature changes from –35 to +40℃ and humidity variations from <10% to 95% RH (noncondensing) over a period of up to 30 days. Alternating cycles simulating conditions of actual 3–4 h flights were specified. Additionally, specimens of the same material were subjected to thermal shock under similar (to the aging scenario) temperature extremes. All specimens were comparatively assessed via experimental procedures involving three-point bending tests performed in both static and dynamic mechanical analysis for a range of temperatures and frequencies, frequency and thermal scans, and finally impact tests. Results indicate that aged materials exhibit increased dynamic stiffness (expressed by the storage moduli) and decreased material damping ability (expressed by the tan δ parameter). Macroscopic assessment of impact test data was performed via stochastic model-based damage detection methodologies. Results indicate that differences in the impact behavior between pristine and aged specimens are statistically detectable and quantifiable, without input from mechanical testing analysis. More importantly, this assessment of aging-induced effects on the specimens corroborates the findings on storage moduli and tan δ from mechanical testing analysis, thus validating the latter.

Piezoelectric dynamic strain monitoring for detecting local seismic damage in steel buildings

This research presents a methodology for damage detection along with a sensing system for monitoring seismic damage in steel buildings. The system extracts the location and extent of local damage, such as fracture at a beam–column connection, from changes in the bending moment distribution in a steel moment-resisting frame. We developed a dynamic strain-based sensing system utilizing piezoelectric film sensors and wireless sensing techniques to estimate the bending moments resisted by individual structural members under small amplitude loadings such as ambient vibrations and minor earthquakes. We introduce a new damage index that extracts local damage information from the comparative study of the dynamic strain responses of the structural members before and after a large earthquake event. The damage detection scheme was examined both analytically and numerically using a simple frame example. Then, the entire local damage detection scheme was verified through a series of vibration tests using a one-quarter-scale steel testbed that simulated seismic damage at member ends.

Data-driven multivariate algorithms for damage detection and identification: Evaluation and comparison

This article is concerned with the experimental validation of a structural health monitoring methodology for damage detection and identification. Three different data-driven multivariate algorithms are considered here to obtain the baseline pattern. These are based on principal component analysis, independent component analysis and hierarchical non-linear principal component analysis. The contribution of this article is to examine and compare the three proposed algorithms that have been reported as reliable methods for damage detection and identification. The approach is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. A woven multilayered composite plate and a simplified aircraft composite skin panel are used as examples to test the approaches. Data-driven baseline patterns are built when the structure is known to be healthy from wavelet coefficients of the structural dynamic responses. Damage is then simulated by adding masses at different positions of the structures. The data from the structure in different states (damaged or not) are then projected into the different models by each actuator in order to generate the input feature vectors of a self-organizing map from the computed components together with squared prediction error measures. All three methods are shown to be successful in detecting and classifying the simulated damages. At the end, a critical comparison is given in order to investigate the advantages and disadvantages of each method for the damage detection and identification tasks.

A statistical reference-free damage identification for real-time monitoring of truss bridges using wavelet-based log likelihood ratios

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

Damage Detection in Nonlinear Structures Using Discrete-Time Volterra Series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinearprocedures are not used currently in practical applications due to the complexity and difficulty forimplementation of such techniques. Therefore, the development of techniques that consider the nonlinearbehavior of structures for damage detection is a research of major importance since nonlineardynamical effects can be erroneously treated as damage in the structure by classical metrics. Thispaper proposes the discrete-time Volterra series for modeling the nonlinear convolution between theinput and output signals in a benchmark nonlinear system. The prediction error of the model in anunknown structural condition is compared with the values of the reference structure in healthy conditionfor evaluating the method of damage detection. Since the Volterra series separate the responseof the system in linear and nonlinear contributions, these indexes are used to show the importanceof considering the nonlinear behavior of the structure. The paper concludes pointing out the mainadvantages and drawbacks of this damage detection methodology.

Crack Location in Beams Using Wavelet Analysis

This paper applies a methodology for damage detection in beams proposed by the authors. The methodology is based on a continuous wavelet analysis of the difference of mode shapes between a damaged state and a reference state. The wavelet transform is used to detect changes in the mode shapes induced by damage. The wavelet coefficients for each mode are added up and normalized to unity in order to obtain a clear and precise damage assessment. A curve fitting approach reduces the effect of experimental noise in the mode shapes. When only a small number of measuring points are available, a cubic spline interpolation technique provides additional “virtual” measuring points. The interpolation technique may also be used when measuring points are not equally spaced. It also serves as a softening technique of the mode shapes when applied, and no curve fitting approach is used in that case. An antisymmetric extension at both ends of the mode shapes is used to avoid the edge effect in the wavelet transform. The paper presents the results obtained for steel beams with an induced crack. Several sizes and locations of the crack have been considered. The paper addresses several issues affecting the accuracy of the proposed methodology, such as the number of measuring points and the effect of the extension, curve fitting and interpolation techniques.

Wavelet-Based Damage Detection in Reinforced Concrete Structures Subjected to Seismic Excitations

The feasibility of using output-only model-free wavelet-based techniques for damage detection in reinforced concrete structures subjected to seismic loads is explored through the analysis of the results of a full scale shake table test of a reinforced concrete bridge column recently performed at the NEES Large High Performance Outdoor Shake Table. The evaluated approaches are based solely in the analysis of the acceleration time histories recorded in the structure. The viability of using numerical models to validate this type of damage detection methodologies is also evaluated. Wavelet analyses were capable of identifying the rebar fracture episodes and partially identified the frequency shifts in the structure as the inelastic demand increased. It was also found that, depending on the methodology employed, the use of numerical models to validate damage detection techniques can oversimplify the actual problem and/or induce spurious irregularities.

Continuous wavelet analysis of mode shapes differences for damage detection

This paper presents a new damage detection methodology for beams. It applies wavelet analysis to locate the damage from changes in the mode shapes (geometric based analysis). The proposed methodology requires the mode shapes of a reference undamaged state as well as those of the potentially damaged one. Once obtained, a continuous wavelet transform is applied to the difference of the mode shape vectors to obtain information of the changes in each of them. Finally, the results for each mode are added up to compute an overall result along the structure. For the addition, the wavelet coefficients of each mode are weighted according to the corresponding variation of the natural frequency. By doing so, emphasis is given on those modes that are more likely to be affected by damage. On the other hand, mode shapes that have not changed their natural frequencies are disregarded. The proposed methodology also includes mathematical techniques to avoid wavelet transform edge effect, experimental noise reduction in mode shapes and creation of new virtual measuring points. It has been validated by experimental analysis of steel beams with cracks of different sizes and at different locations. The results show that the method is sensitive to little damage. The paper analyses the severity threshold of damage and the required number of sensors to obtain successful results.

Fully coupled electromechanical elastodynamic model for guided wave propagation analysis

Physics-based computational models play a key role in the study of wave propagation for structural health monitoring and the development of improved damage detection methodologies. Due to the complex nature of guided waves, accurate and efficient computation tools are necessary to investigate the mechanisms responsible for dispersion, coupling, and interaction with damage. In this article, a fully coupled electromechanical elastodynamic model for wave propagation in a heterogeneous, anisotropic material system is developed. The final framework provides the full three-dimensional displacement and electrical potential fields for arbitrary plate and transducer geometries and excitation waveform and frequency. The model is validated theoretically and proven computationally efficient. Studies are performed with surface-bonded piezoelectric sensors to gain insight into the physics of experimental techniques used for structural health monitoring. Collocated actuation of the fundamental Lamb wave modes is modeled over a range of frequencies to demonstrate mode tuning capabilities. The displacement of the sensing surface is compared to the piezoelectric sensor electric potential to investigate the relationship between plate displacement and sensor voltage output. Since many studies, including the ones investigated in this article, are difficult to perform experimentally, the developed model provides a valuable tool for the improvement of structural health monitoring techniques.

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

A fundamental problem in structural damage detection is to define an efficient feature to calculate a damage index. Furthermore, due to perturbations from various sources, we also need to define a rigorous threshold whose overtaking indicates the presence of damages. In this article, we develop a robust damage detection methodology based on principal component analysis. We first present an original damage index based on projection of the separation matrix, and then, we drive a novel adaptive threshold that does not rely on statistical assumptions. This threshold is analytic, and it is based on matrix perturbation theory. The efficiency of the method is illustrated using simulations of a composite smart structure and experimental results performed on a conformal load-bearing antenna structure laboratory test.

Damage detection by using FBGs and strain field pattern recognition techniques

A novel methodology for damage detection and location in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (principal component analysis) and damage indices (T2 and Q). We propose the use of fiber Bragg gratings (FBGs) as strain sensors.

Energy Harvesting for Aerospace Structural Health Monitoring Systems

Recent research into damage detection methodologies, embedded sensors, wireless data transmission and energy harvesting in aerospace environments has meant that autonomous structural health monitoring (SHM) systems are becoming a real possibility. The most promising system would utilise wireless sensor nodes that are able to make decisions on damage and communicate this wirelessly to a central base station. Although such a system shows great potential and both passive and active monitoring techniques exist for detecting damage in structures, powering such wireless sensors nodes poses a problem. Two such energy sources that could be harvested in abundance on an aircraft are vibration and thermal gradients. Piezoelectric transducers mounted to the surface of a structure can be utilised to generate power from a dynamic strain whilst thermoelectric generators (TEG) can be used to generate power from thermal gradients. This paper reports on the viability of these two energy sources for powering a wireless SHM system from vibrations ranging from 20 to 400Hz and thermal gradients up to 50°C. Investigations showed that using a single vibrational energy harvester raw power levels of up to 1mW could be generated. Further numerical modelling demonstrated that by optimising the position and orientation of the vibrational harvester greater levels of power could be achieved. However using commercial TEGs average power levels over a flight period between 5 to 30mW could be generated. Both of these energy harvesting techniques show a great potential in powering current wireless SHM systems where depending on the complexity the power requirements range from 1 to 180mW.

Structural health monitoring of a stress-ribbon footbridge

This work describes the development and implementation of a structural health monitoring system on a stress-ribbon footbridge. In a first part, it characterises the implemented continuous dynamic monitoring system and the application of automated operational modal analysis to analyse the variation of modal properties estimates along several years. A correlation analysis is then conducted showing that environmental and operational factors (e.g. temperature and pedestrian traffic) induce significant nonlinear effects on the modal frequency estimates, which may mask subtle early damage. Taking into account linear relations between frequency estimates of different modes, the linear Principal Component Analysis (PCA) is applied to remove those effects. Novelty analysis of the residual errors of PCA is used to build a statistical damage indicator for long term structural health monitoring. Finally, the efficiency of the described damage detection methodology is evidenced by simulating some realistic damage scenarios based on an experimentally validated finite element model and observing the clear deviation of the damage indicator. It is demonstrated that such a dynamic monitoring system can serve as an effective tool for long term bridge health monitoring.

MUSIC‐ANN Analysis for Locating Structural Damages in a Truss‐Type Structure by Means of Vibrations

Abstract: This article presents a methodology for damage detection, location, and quantification based on vibration signature analysis and a comprehensive experimental study to assess the utility of the proposed structural health monitoring applied to a five‐bay truss‐type structure. The MUSIC algorithm introduced first by Jiang and Adeli for health monitoring of structures in 2007 is fused with artificial neural networks for an automated result. The developed methodology is based on feeding the amplitude of the natural frequencies as input of an artificial neural network, being the novelty of the proposed methodology its ability to identify, locate, and quantify the severity of damages with precision such as: external and internal corrosion and cracks in an automated monitoring process. Results show the proposed methodology is effective for detecting a healthy structure, a structure with external and internal corrosion, and a structure with crack. Therefore, the proposed fusion of MUSIC‐ANN algorithms can be regarded as a simple, effective, and automated tool without requiring sophisticated equipment, toward establishing a practical and reliable structural health monitoring methodology, which will help to evaluate the condition of the structure, in order to detect damages early and to make the corresponding maintenance decisions in the structures.

Low-velocity impact damage monitoring of a sandwich composite wing

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low-energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient structural health monitoring methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this article, a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical feature matrices, composed of features extracted from the time and frequency domains, are developed. Kernel principal component analysis is used to compress and classify the statistical feature matrices. Compared with traditional principal component analysis algorithm, kernel principal component analysis method shows better feature clustering and damage quantification capabilities. A new damage index, formulated using the Mahalanobis distance, is defined to quantify impact damage. The developed methodology has been validated using low-velocity impact experiments with a sandwich composite wing.