Damage Detection Algorithm Research Articles

A multi-delay-and-sum imaging algorithm for damage detection using piezoceramic transducers

The traditional delay-and-sum imaging algorithm usually requires sending an excitation pulse at each piezoceramic transducer and obtains a damage image by drawing only ellipses. A multi-delay-and-sum imaging algorithm is proposed for damage detection of thin-plate-like structures using sparse piezoceramic transducers. Compared with the traditional delay-and-sum imaging algorithm, the proposed algorithm sends only one excitation pulse for each detection. A reflection coefficient is employed in the proposed method to cancel the artifacts caused by the boundary reflection signals, and the reflection coefficient is determined by the distribution of piezoceramic transducers and strength of the reflection signals. An additional time compensation due to the excitation pulse is also made to reduce the error of damage locating. To increase the image pixel value of a damage, the damage image is obtained by drawing both ellipses and hyperbolas with transmitter–sensor pair signals. The experimental results obtained on an aluminum alloy plate demonstrate that the proposed multi-delay-and-sum imaging algorithm can identify a bonded mass damage efficiently and accurately.

Nondestructive evaluation algorithm of fatigue cracks and far-side corrosion around a rivet fastener in multi-layered structures

This research proposes a nondestructive inspection system for inspecting and localizing corrosion and fatigue cracks around rivets in air-intake structures. The system uses 64 InSb Hall sensor elements arrayed at a high spatial interval of 0.52 mm. Rivet detection and damage detection algorithms will be proposed. Analysis of the receiver operating characteristic curve and Probability of detection (POD) will be carried out to evaluate the performance of the system and detection algorithms. Artificial corrosion around a rivet with a minimum volume of 11.02 mm3 could be detected with 90/95% POD and artificial fatigue crack with minimum length of 2.95 mm from rivet body.

A framework for data compression and damage detection in structural health monitoring applied on a laboratory three-story structure

Structural Health Monitoring (SHM) is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.

Structural damage detection using ARMAX time series models and cepstral distances

A novel damage detection algorithm for structural health monitoring using time series model is presented. The proposed algorithm uses output-only acceleration time series obtained from sensors on the structure which are fitted using Auto-regressive moving-average with exogenous inputs (ARMAX) model. The algorithm uses Cepstral distances between the ARMAX models of decorrelated data obtained from healthy and any other current condition of the structure as the damage indicator. A numerical model of a simply supported beam with variations due to temperature and operating conditions along with measurement noise is used to demonstrate the effectiveness of the proposed damage diagnostic technique using the ARMAX time series models and their Cepstral distances with novelty indices. The effectiveness of the proposed method is validated using the benchmark data of the 8-DOF system made available to public by the Engineering Institute of LANL and the simulated vibration data obtained from the FEM model of IASC-ASCE 12-DOF steel frame. The results of the studies indicate that the proposed algorithm is robust in identifying the damage from the acceleration data contaminated with noise under varied environmental and operational conditions.

Reliability Assessment of SHM Methodologies for Damage Detection

Probability-based imaging which illustrates a distribution map of probability of damage presence in structures is a diagnostic method well established for damage detection in sensorized structures. Since the quality of the recorded signal is directly linked to the reliability of the diagnostic outcome, the assessment of robustness of the damage detection methodology is of high significance. In this paper, robustness and reliability of the current probability based imaging algorithms have been assessed for detecting BVID in a composite panel. Consequently, a proposed outlier analysis and DI probability distribution damage detection algorithm was shown to improve the reliability of the detection method.

Structural model updating using incomplete transfer function and modal data

Structural damage detection by non-destructive techniques has been investigated widely by researchers in recent years, and several response-based Structural Health monitoring techniques have been developed. In this paper, a vibration-based damage detection algorithm through model updating utilizing incomplete frequency response function and modal data is presented. First, a modified sensitivity equation is presented to relate the changes of mode shapes to the changes of unknown structural parameters. Then, the developed equations are used to correlate changes of frequency response functions to the changes of structural parameters. Parameter estimation is conducted through least square solution of sensitivity equations. In order to prove the capability of the proposed method for structural damage detection purpose, a numerical example using error contaminated data is presented. Results exhibit robustness of method against measurement and mass modeling errors.

Fatigue crack detection and identification by the elastic wave propagation method

In this paper the elastic wave propagation phenomenon was used to detect the initiation of the fatigue damage in isotropic plate with a circular hole. The safety and reliability of structures mostly depend on the effectiveness of the monitoring methods. The Structural Health Monitoring (SHM) system based on the active pitch-catch measurement technique was proposed. The piezoelectric (PZT) elements was used as an actuators and sensors in the multipoint measuring system. The comparison of the intact and defected structures has been used by damage detection algorithm. One part of the SHM system has been responsible for detection of the fatigue crack initiation. The second part observed the evolution of the damage growth and assess the size of the defect. The numerical results of the wave propagation phenomenon has been used to present the effectiveness and accuracy of the proposed method. The preliminary experimental analysis has been carried out during the tension test of the aluminum plate with a circular hole to determine the efficiency of the measurement technique.

Decentralised one-class kernel classification-based damage detection and localisation

In this paper, a data-based damage detection algorithm that uses a novel one-class kernel classifier for detection and localisation of damage is presented. The demands of wireless sensing are carefully considered in the development of this fully decentralised and automated methodology. The one-class kernel classifier proposed in this paper is trained through a faster and simpler to implement iterative procedure than other kernel classification methods, while retaining the same advantages over parametric methods, making it especially attractive for embedded damage detection. Acceleration time series at each sensor location are processed into autoregressive and continuous wavelet transform-based damage-sensitive features. Baseline values of these features are used to train the classifier, which can then classify features from new tests as damaged or undamaged, as well as outputting a localisation index, which can be used to identify the location of damage in the structure. This methodology is evaluated using acceleration data taken from a steel-frame laboratory structure under various damage scenarios. A number of parametric studies are also conducted to investigate the effect of sampling frequency and baseline data sample size. Copyright © 2016 John Wiley & Sons, Ltd.

Optimal sensor placement for maximum area coverage (MAC) for damage localization in composite structures

In this paper an optimal sensor placement algorithm for attaining the maximum area coverage (MAC) within a sensor network is presented. The proposed novel approach takes into account physical properties of Lamb wave propagation (attenuation profile, direction dependant group velocity due to material anisotropy) and geometrical complexities (boundary reflections, presence of openings) of the structure. A feature of the proposed optimization approach lies in the fact that it is independent of characteristics of the damage detection algorithm (e.g. probability of detection) making it readily up-scalable to large complex composite structures such as aircraft stiffened composite panel. The proposed fitness function (MAC) is independent of damage parameters (type, severity, location). Statistical analysis carried out shows that the proposed optimum sensor network with MAC results in high probability of damage localization. Genetic algorithm is coupled with the fitness function to provide an efficient optimization strategy.

A delay-and-Boolean-ADD imaging algorithm for damage detection with a small number of piezoceramic transducers

The delay-and-sum (DAS) imaging algorithm usually sends an excitation signal at each piezoceramic transducer and obtains a defect image by using transmitter–sensor pair signals to draw ellipses or hyperbolas. A delay-and-Boolean-ADD (DABA) imaging algorithm is developed for defect detection of plate-like structures with a small number of piezoceramic transducers. This new method requires sending only one excitation signal for each detection, and obtains a better defect image by employing Boolean ADD operation instead of addition or multiplication operation in the DAS algorithm. A reflection coefficient is introduced in the new algorithm to attenuate the signals reflected from the boundary. The widely used envelop-detection method based on Hilbert-transformation is replaced by a new envelop-detection technique based on a local maximum value to increase the accuracy of locating. An additional time shift due to the excitation signal itself is also considered to decrease the location error. The results of the experiments conducted on an aluminum plate indicate that the proposed DABA imaging algorithm combining with the new techniques can detect a bonded mass defect accurately and efficiently.

Compressive sensing for efficient health monitoring and effective damage detection of structures

Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) – a very recent development – introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

Ultrasonic guided wave propagation and disbond identification in a honeycomb composite sandwich structure using bonded piezoelectric wafer transducers

A coordinated theoretical, numerical, and experimental study is carried out in an effort to understand ultrasonic guided wave propagation and interaction with disbond, as well as, to identify disbond in a honeycomb composite sandwich structure using surface-bonded piezoelectric wafer transducers. In contrast to most of the work done previously, a fast and efficient two-dimensional semi-analytical model based on global matrix method is used to study dispersion characteristics as well as transient response of the healthy honeycomb composite sandwich structure subjected to relatively high-frequency piezoelectric wafer transducer excitations. Numerical simulations are then conducted using commercially available finite element package, ABAQUS, in order to explore guided wave propagation mechanisms due to the presence of disbond. Numerical simulations are further broadened to investigate the effect of disbond size on the amplitudes and group velocities of propagating guided wave modes. A good agreement is observed between the theoretical, numerical and experimental results in all cases studied. It is noticed that the presence of disbond, in particular, amplifies the first anti-symmetric (A0) mode and increases its group velocity. Finally, based on these modal behaviors, the location of an unknown disbond, within the piezoelectric wafer transducer array is experimentally determined by applying a probability-based damage detection algorithm.

Reference-Free Damage Identification in Plate-Like Structures Using Lamb-Wave Propagation with Embedded Piezoelectric Sensors

AbstractStructural health monitoring (SHM) has been adopted in the aircraft and civil industries over the last two decades. The ultrasonic guided wave propagation technique is a promising damage detection approach in airplane thin wall structures because of their long distance traveling ability and low attenuation. In fact, by efficiently designing the pattern of sensors one can effectively pick up the changes that happen in propagated signals due to presence of any kind of structural damage. This detection approach could be effectively assisted by numerical simulation techniques like finite-element modeling. To obtain a damage detection algorithm based on instantaneous baseline data, the finite-element technique is employed for simulation of wave propagation in thin wall structures. Also, the influence of artificial crack (cut-damage) on propagated Lamb waves is investigated. An instantaneous baseline damage detection based on cross-correlation (CC) analysis is carried out to detect the damage, and the r...

A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection

Structural damage detection (SDD) is a challenging task in the flied of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters,such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

Transducer placement optimisation scheme for a delay and sum damage detection algorithm

Summary In this work, a transducer placement scheme based on wave propagation is proposed, which enhances damage localisation. The method was tailored to seek an optimal transducer network placement for a delay and sum damage detection algorithm. The proposed method determines a coverage index map and utilises a genetic algorithm to determine an optimal transducer network. It can also minimise the impact of faulty transducers, incorporate the effect of stiffeners and different damage types. The method is initially verified using numerically simulated signals. The optimal network outperformed the suboptimal for detection of holes and debonding in a stiffened panel. It is also shown that the coverage index reflected the localisation accuracy. The method is then validated with experimental results and the generated optimal transducer network compared with a suboptimal arrangement. The optimal network is shown to locate an actual crack with significantly higher accuracy than the suboptimal arrangement. © 2016 The Authors. Structural Control and Health Monitoring published by John Wiley & Sons, Ltd.

Identification of disbond and high density core region in a honeycomb composite sandwich structure using ultrasonic guided waves

The aim of this study is to identify disbond and high-density (HD) core region in a honeycomb composite sandwich structure (HCSS) using ultrasonic guided waves (GWs) and surface-bonded piezoelectric wafer transducers (PWTs). Towards this, an organized theoretical, numerical and experimental study has been carried out in order to understand the characteristics of GW propagation in a HCSS. A global matrix method based efficient and fast two dimensional (2D) semi-analytical model is used to study transient response and dispersion characteristics of the healthy HCSS under PWT excitations. Numerical simulation of GW propagation in HCSS with disbond and HD-core is then carried out using finite element package, ABAQUS. Laboratory experiments are then carried out to validate the theoretical and numerical results. A good agreement is observed between the theoretical, numerical and experimental results in all cases studied. It is found that the presence of HD-core region leads to decrease in amplitude of the propagating GW modes and the presence of disbond leads to substantial amplification of the primary anti-symmetric mode. Finally, based on these changes in modal behaviors, the location and size of unknown disbond and HD-core region within the PWT array are experimentally determined using a probability based damage detection algorithm.

Damage detection in multi-wire cables using guided ultrasonic waves

Structural Health Monitoring systems are developed to cost-efficiently prevent failure of mechanical and civil structures, and to predict the structure’s residual life. In this work, a damage detection algorithm based on the Hilbert transform of the recorded signals from induced guided ultrasonic waves is presented. By means of this algorithm, damage localization in multi-wire cables is performed through a time-of-flight analysis of the wave packets. The algorithm is fully automated and distinguishes between wave packets from different waves independently. Its applicability is analyzed for laboratory experiments on a single cylindrical wire and on multi-wire cables. As an additional damage indicator, second harmonic waves are evaluated. Furthermore, the possibility to perform damage identification by evaluating the waves’ amplitudes is analyzed. The amplitudes are compared with reference data from a novel hybrid finite-boundary element method.

A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges

This paper proposes a novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.

A new intelligent algorithm for damage detection in frames via modal properties

ABSTRACTIn this article, an intelligent method to detect, locate and quantify structural damages is presented via an optimization model. To predict the damage location and severity, the theory of pseudo-residual force vector (RFV) is applied. The proposed method can identify damages based on only a few mode shapes of the structure that can be easily obtained by a dynamic test. The objective function is defined as a minimum difference between the numerical and experimental variables in the RFV and the gravitational search algorithm is employed as a meta-heuristic technique for solving this optimization problem. The efficiency of the proposed method is investigated through the numerical examples with different damage scenarios. In the examples, the experimental data were simulated numerically using a finite element model of the structure and as demonstrated, it is possible to identify the damages with a reasonable level of accuracy while considering noise effects.

A two-step approach for damage detection in laminated composite structures using modal strain energy method and an improved differential evolution algorithm

The paper presents a two-step approach based on modal strain energy method and an improved differential evolution algorithm for damage detection in laminated composite structures. First, the modal strain energy based method is employed to identify a set of potential damaged elements. Then, the improved differential evolution algorithm is utilized to minimize the function of mode shape error with design variables relating to the extent of identified damaged elements. Here, the function of mode shape error is defined by the shift between the mode shape of the damaged structure and that of the healthy structure. The proposed approach is applied for a cross-ply (0°/90°/0°) laminated composite beam and a cross-ply (0°/90°/0°) square laminated composite plate with multiple damaged elements. In addition, the effect of noise on the accuracy of damage identification is also investigated. Numerical results show that the proposed approach is effective for damage detection in laminated composite beam and plate structures for both cases with noise and without noise.