Vibration-based Damage Detection Research Articles

Structural damage detection based onl1regularization using natural frequencies and mode shapes

Conventional vibration-based damage detection methods employ the Tikhonov regularization in model updating to deal with the problems of underdeterminacy and measurement noise. However, the Tikhonov regularization technique tends to provide over smooth solutions that the identified damage is distributed to many structural elements. This result does not match the sparsity property of the actual damage scenario, in which structural damage typically occurs at a small number of locations only in comparison with the total elements of the entire structure. In this study, an l1 regularization-based model updating technique is developed by utilizing the sparsity of the structural damage. Both natural frequencies and mode shapes are employed during the model updating. A strategy of selecting the regularization parameter for the l1 regularization problem is also developed. A numerical and an experimental examples are utilized to demonstrate the effectiveness of the proposed damage detection method. The results showed that the proposed l1 regularization-based method is able to locate and quantify the sparse damage correctly over a large number of elements. The effects of the mode number on the damage detection results are also investigated. The advantage of the present l1 regularization over the traditional l2 regularization method in damage detection is also demonstrated.

Estimation of efficiency of vibration damage detection in stepped shaft of steam turbine

The vibration based damage detection has practically no alternative as applied to the rotating shafts of steam turbines during operation. However, taking into account the heavy operating conditions of turbines and the complex shape of the turbines shaft, the choice of most effective method of vibration diagnostics is not an easy problem. To solve this problem, there was developed a relatively simple procedure of comparative evaluation of the effectiveness of vibration diagnostics of damage. The procedure is based on the determination of the change of shaft’s compliance caused by a crack with the use of linear fracture mechanics. It was demonstrated as applied to the stepped rotors and shafting of steam turbine. The comparative analysis of efficiency of vibration diagnostics was performed based on the change of natural bending, longitudinal or torsional frequency of vibration to detect transverse or longitudinal crack in rotors and shafting of steam turbine K-200-130.

1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.

Cooperative Coevolution with Dynamic Species-Size Strategy for Vibration-Based Damage Detection in Plates

Vibration-based damage detection is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage occurs. This paper proposes dynamic species-size strategy in cooperative coevolution concept. The resulting algorithm, cooperative coevolutionary genetic algorithm with dynamics species-size (CCGADSS), is used as the optimization algorithm in the vibration-based damage detection in plates. The objective function is numerically calculated from the difference between experimentally vibration characteristics and numerically evaluated vibration characteristics of the predicted damage. In finite element model for objective calculation, the plates are equally divided into 64 elements. There are 2 different cases with dissimilar occurred damage in plates are considered. In first case, the plate hase only one region consisting of 4 elements which are together connected and have same damage. In second case, there are 5 separated elements which are damaged differently. In order to demonstrate the performance of the dynamic species-size strategy, 3 optimization algorithms, which are genetic algorithm (GA), cooperative coevolutionary genetic algorithm (CCGA), and CCGADSS. The results indicate that CCGADSS is superior to GA and CCGA. Moreover solutions obtained using CCGADSS are quite close the actual damage. These results show that the dynamic species-size strategy can enhance performance of cooperative coevolution concept.

Strain sensors optimal placement for vibration-based structural health monitoring. The effect of damage on the initially optimal configuration

We revisit the optimal sensor placement of engineering structures problem with an emphasis on in-plane dynamic strain measurements and to the direction of modal identification as well as vibration-based damage detection for structural health monitoring purposes. The approach utilized is based on the maximization of a norm of the Fisher Information Matrix built with numerically obtained mode shapes of the structure and at the same time prohibit the sensorization of neighbor degrees of freedom as well as those carrying similar information, in order to obtain a satisfactory coverage. A new convergence criterion of the Fisher Information Matrix (FIM) norm is proposed in order to deal with the issue of choosing an appropriate sensor redundancy threshold, a concept recently introduced but not further investigated concerning its choice. The sensor configurations obtained via a forward sequential placement algorithm are sub-optimal in terms of FIM norm values but the selected sensors are not allowed to be placed in neighbor degrees of freedom providing thus a better coverage of the structure and a subsequent better identification of the experimental mode shapes. The issue of how service induced damage affects the initially nominated as optimal sensor configuration is also investigated and reported. The numerical model of a composite sandwich panel serves as a representative aerospace structure upon which our investigations are based.

Damage detection of simply supported reinforced concrete beam by S transform

Signal processing is the key component of vibration-based structural damage detection. The S transform is variable window of short time Fourier transform (STFT) or an extension of wavelet transform (WT). The goal of using S transform is to extract subtle changes in the vibration signals in order to detect and quantify the damage in the structure. This paper presents the concentrated load is applied to the simply supported reinforced concrete beam and adopting the stepwise loading method, the vibration signals of each loading and unloading state is obtained by using the hammer impact. Then the vibration data of the reinforced concrete beam pre-damage and post-damage is analysed by S transform. Experimental result shows the potential ability of S transform in identifying peak energy changes and multiple reflections with different loading force state.

Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks

AbstractStructural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridg...

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

Damage-sensitive features such as natural frequencies are widely used for structural health monitoring; however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde–Buzo–Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, the proposed method is the only successful method to deal with the non-linear dependency among features and detect the structural damage timely.

Optimization the inner product vector method and its application to structural health monitoring

Fast methods to identification and recognition of structural defects are important issues for the industry. In recent years, a growing interest has been on quick structural inspection to significantly reduce inspection’s cost and time, while minimizing the number of Non-Destructive Testing (NDT). Investigations on damage detection methods based on vibration monitoring have been developed in the last decade. This paper presents a study on Inner Product Vector (IPV) method. The IPV method is a new vibration based damage detection technique. In this method vibration responses are measured before and after damage occurrence. The vibration responses include the time domain acceleration (or displacement or velocity). The IPV method has the potential to significantly reduce costs by minimizing the need for NDT methods. For damage detection via the IPV method, a threshold should be selected for classifying the damaged and undamaged sections of a structure. Proper determination of the threshold value requires selection of Confidence Interval Factor (CIF). In this study, a new algorithm for the IPV method is suggested in which a new optimized model for damage detection is presented. Aforementioned optimized model can provide an accurate value for the CIF. To ascertain the exact CIF, the damage detection method is simulated. An accurate threshold makes the IPV method capable to accurately detect damages. The method has been verified by means of an FEM model as well as an experimental case study. The results show that the optimization process can be used as a reference to ascertain value for the CIF. The IPV method can be used as a Structural Health Monitoring (SHM) method, but it’s necessary to optimize the IPV method for each sample.

Sensitivity analysis of inverse algorithms for damage detection in composites

Vibration-based damage detection is a promising structural health monitoring technique for identifying delaminations in composite structures. However, its accuracy may be adversely affected by measurement errors and the discrepancy between the real composite structures and their numerical models. Therefore, it is of critical importance to study the effect of such noise on the prediction accuracy. The ideal way to do this is to analyze the noise effects based on real measurements on large number of specimens. However, evidently, this is cost and time intensive in terms of materials, manufacturing and labor. An alternative way is to add artificial noise to the structural dynamic parameters obtained from numerical model and study diverse cases using this modified numerical data. In this paper, different levels of random artificial noise (1%, 2%, 3% and 5%) are added to the numerical frequencies. In order to prevent any undesirable bias, 10 different sets of random noise, both uniformly and normally distributed, are employed at each noise level for the same frequencies. The frequency shifts from these 10 test cases (at each noise level) were then input into three inverse algorithms, namely graphical technique (GT), surrogate-assisted optimization (SAO) and artificial neural network (ANN), to predict the location, size and interface of delaminations in a composite beam. The predictions were subsequently compared to the actual damage parameters to evaluate the effect of the noise on the prediction accuracies. SAO and ANN were also tested with 100 cases of noise and then further with Monte Carlo simulation with 100,000 samples in ANN to verify the results. Additional noise sensitivity tests were carried out on beams with different delamination sizes and interfaces, to investigate the dependence of noise effect on the damage parameters themselves. Results show that the trends in prediction errors with increasing noise levels become more consistent as the number of samples increased. With 100,000 samples, the predictions by ANN with uniformly distributed noise and Gaussian noise were nearly indistinguishable. Furthermore, prediction accuracies are also affected by the location and size of the delamination. Among the three inverse algorithms, the performance of ANN was found to be less reliable in presence of noise compared to SAO and GT.

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

Damage detection for beam structures based on local flexibility method and macro-strain measurement

Many vibration-based global damage detection methods attempt to extract modal parameters from vibration signals as the main structural features to detect damage. The local flexibility method is one promising method that requires only the first few fundamental modes to detect not only the location but also the extent of damage. Generally, the mode shapes in the lateral degree of freedom are extracted from lateral vibration signals and then used to detect damage for a beam structure. In this study, a new approach which employs the mode shapes in the rotary degree of freedom obtained from the macro-strain vibration signals to detect damage of a beam structure is proposed. In order to facilitate the application of mode shapes in the rotary degree of freedom for beam structures, the local flexibility method is modified and utilized. The proposed rotary approach is verified by numerical and experimental studies of simply supported beams. The results illustrate potential feasibility of the proposed new idea. Compared to the method that uses lateral measurements, the proposed rotary approach seems more robust to noise in the numerical cases considered. The sensor configuration could also be more flexible and customized for a beam structure. Primarily, the proposed approach seems more sensitive to damage when the damage is close to the supports of simply supported beams.

Modal parameter identification and vibration based damage detection of a multiple cracked cantilever beam

Abstract This paper presents a detailed investigation on the modal parameter identification and vibration based damage detection of a multiple cracked cantilever beam with hollow circular cross-section. To consider multiple crack effects, a cantilever beam including cracks is considered for six damage scenarios. Finite element models are constituted in ANSYS software for numerical solutions. The results are validated by experimental measurements. Ambient vibration tests are performed to extract the dynamic characteristics using Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI) methods. Calculated and measured natural frequencies and mode shapes for undamaged and damaged beams are compared with each other. Automated model updating is carried out using the modal sensitivity method based on Bayesian parameter estimation to minimize the differences for damage detection. In addition, modal assurance criterion (MAC) and coordinated modal assurance criterion (COMAC) factors are obtained from the mode shapes and two set of measurements to establish the correlation between the measured and calculated values for damage location identification.

Truncation error analysis on modal flexibility-based deflections: application to mass regular and irregular structures

It is of interest in the fields of vibration-based structural identification and damage detection to analyze the truncation effects introduced on modal flexibility (MF) based deflections that are estimated using only a subset of structural modes. To address this problem, an approach for truncation error analysis on MF-based deflections of structural systems subjected to a generic load is proposed in this paper. The approach is based on the determination of the relative contribution of each mode to the deflection by means of a proposed load participation factor (LPF). This factor, as derived analytically, depends both on the applied load and on the distribution of the structural masses. The validation of the proposed approach was carried out both on numerical models of shear-type frame buildings and on experimental data of a steel frame structure tested under ambient vibrations (i.e. the benchmark study sponsored by the IASC-ASCE Task Group on SHM). In both cases, results show that the LPF factors can give an a priori indication of the truncation effects expected on the MF-based deflections. The relationship between the proposed approach and the approach based on the mass participation factors, introduced by Zhang and Aktan (1998) for the case of uniform load (UL) deflections, is discussed since the two approaches are equal only if a special load, which is a mass proportional load (MPL), is considered. Thus, the application of this MPL load for mass irregular structures is also investigated. Numerical analyses performed both on a shear-type frame building and on a simply-supported beam, showed that for the great majority of the analyzed configurations, the truncation errors on the MF-based deflections due to the MPL are lower compared to those related to the UL.

A Global Expectation–Maximization Approach Based on Memetic Algorithm for Vibration-Based Structural Damage Detection

This paper proposes a novel unsupervised damage detection approach based on a memetic algorithm that establishes the normal or undamaged condition of a structural system as data clusters through a global xpectation–maximization technique, using only damage-sensitive features extracted from output-only vibration measurements. The health state is then discriminated by considering the Mahalanobis squared distance between the learned clusters and a new observation. The proposed approach is compared with state-of-the-art ones by taking into account real-world data sets from the Z-24 Bridge (Switzerland), where several damage scenarios were performed. The results indicated that the proposed approach can be applied in structural health monitoring applications where life safety, economic, and reliability issues are the most important motivations to consider.

Damage detection under varying temperature using artificial neural networks

To avoid false alarms for vibration-based structural damage detection methods, temperature effects on damage-sensitive features should be eliminated. In this paper, a novel two-step damage identification method combining a multilayer neural network and novelty detection is developed to differentiate the changes in natural frequencies (one of the most commonly used damage features that can be obtained reliably and relatively easily) due to damage from those induced by temperature variations. In the first step, a multilayer artificial neural network, which resembles an auto-associative neural network but uses temperature variables in addition to the frequencies as the inputs, is explored to identify patterns in frequencies of undamaged structures under varying temperatures. Euclidean distance is then utilized as a novelty index to quantify the discordancy between patterns in undamaged cases and candidate cases. Numerical studies using a simply supported beam and finite element models based on an experimental grid structure, which simulate different levels of stiffness reductions under varying temperature conditions, are used to verify the detectability and robustness of the proposed approach. It is shown that the incorporation of the proposed artificial neural network with novelty detection enables one to robustly distinguish damage occurrence and severity regardless of temperature variations and noise perturbations. Using an unsupervised learning scheme, the proposed approach transforms a multivariate analysis using modal frequencies and temperature data into a straightforward univariate discordancy test using the novelty index. Given these competitive advantages, this approach is very attractive for the development of an automated continuous monitoring system in practical applications.

Vibration-based damage detection of concrete gravity dam monolith via wavelet transform

This study has presented a method to estimate the damage location in the concrete gravity dam monolith. The first four vibration mode of the highest monolith of the Koyna dam was estimated and analyzed by the combined discrete and continuous wavelet transform. The position of the damage scenarios was identified by using bior 3.9 wavelet in the spatial variation of the data response. The sensitivity of wavelet to random signal noise was investigated by using noise to displacement response of cracked monolith. Three noise levels were introduced in the model and the capacity of this method to detect damage from noisy data was discussed.

Bridge Damage Detection Based on Vibration Data: Past and New Developments

Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study of the S101 Bridge is also presented to test the damage sensitivity a chosen methodology.

Efficiency analysis of vibration based crack diagnostics in rotating shafts

The vibration based damage detection has practically no alternative as applied to the rotating shafts of steam turbines during operation. A lot of vibration method of damage detection are developed till present time. But the efficiency of all these methods is very dependent on the type of structure, way of its deformation and on the type of damage. At a moment practical engineers have no instrument to make right choice of most appropriate method of damage detection for some or other structure.The comparative estimation of efficiency of different vibration diagnostics methods to detect damage of sub-critical size is complex and time-consuming process. So there was developed quite simple method of selection of most efficient vibration method of damage detection for a certain structure with a certain type of crack and at a certain type of deformation. This method is based on the determination of relative change of shaft’s compliance caused by a crack with the use of linear fracture mechanics. As an example there was performed the comparative sensitivity analysis of several vibration methods of damage detection to the presence of longitudinal and transverse crack in a shaft at bending and torsional vibration.

Benchmark Studies for Bridge Health Monitoring Using an Improved Modal Strain Energy Method

Insufficient available dynamic characteristics data of real structures during the service and prior to damage is a concern in Structural Health Monitoring (SHM) employing Vibration Based Damage Detection (VBDD) techniques. The issue becomes more intense with complexity of the structure. One remedy for this problem is to apply new methodologies to the same structure as the benchmark structure having measured data available at different cases. Hence, the reliability of the proposed approach is typically confirmed in reality for that type of structures. This study aims to examine the application of an improved two-stage Modal Strain Energy (MSE) method to a benchmark bridge available, whereas the MSE method has already been numerically and experimentally verified. For this purpose, a steel truss bridge model is numerically simulated. Different damage scenarios, affected by up to five percent noise are considered and the first five vertical mode shapes are used. The results show that the proposed method is proper for health monitoring of complex bridges and accurately identifies the damage in the bridge model under consideration. The findings of this paper can confidently contribute to academic studies and the bridge industry to realize the genuine condition and behaviour of complex bridges during the damage.