Optimization of Wavelet Packets Analysis for Damage Detection in Composite Materials

2007 ◽  
Vol 334-335 ◽  
pp. 1149-1152
Author(s):  
Long Yu ◽  
Yun Ju Yan ◽  
Jie Sheng Jiang ◽  
Li Cheng
Keyword(s):  
Composite Materials ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Wavelet Packets ◽  
Structural Vibration ◽  
Time Saving ◽  
Before And After ◽  
Vibration Responses ◽  
Artificial Neural Network Ann

A method based on entropy-based criteria is present to choose the optimal decomposition of Wavelet Packets Analysis (WPA) for damage detection in composite materials. The structural damage indexes constructed based on energy spectrum variation of the structural vibration responses decomposed using WPA before and after the occurrence of structural damage usually generate a complete binary tree to calculate its elements. Date mining is carried out in this paper by adoption entropy as the criteria to choose the optimal decomposition tree. In the decomposition process, only the sub-signals which contain main information of the original signal are decomposed to generate next level sub-signals. New damage index is constructed based on the optimal decomposition. Then the dimension of the damage index is reduced while still keeping its sensitive to damage. Whether Artificial Neural Network (ANN) or genetic algorithm (GA) is used in the further process of telling structural damage status from damage index, this reduction will make remarkable time saving.

scholarly journals Damage detection of beam bridge under a moving load using Auto-encoder

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Juntao Wu ◽  
Zhenhua Nie
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Moving Load ◽  
Damage Index ◽  
Structural Vibration ◽  
Vibration Data ◽  
Vibration Responses ◽  
Hidden Layer ◽  
The Time Domain

A novel damage detection approach based on Auto-encoder neural network is proposed to identify damage in beam-like bridges subjected to a moving mass. In this approach, several sensors are used to measure structural vibration responses during a mass moving across the bridge. An auto-encoder (AE) neural network is designed to extract features from the measured responses. A fixed moving window is used to cut out the time-domain responses to generate inputs of the AE neural network. Moreover, some constraints are applied on the hidden layer to improve the performance of the AE network in training process. When the training is complete, the encoder was regarded as a feature extractor. And the damage index is defined as the cosine distance between two feature vectors obtained from adjacent data windows. By moving the window along the measured vibration data, we can calculate a damage index series and locate the damage position of the structure. To demonstrate the performance of the proposed method, numerical simulation is carried out. The results show that the proposed method can accurately locate both single and multiple damages using acceleration response. It infers the proposed method is promising for structural damage detection.

Bridge condition monitoring under moving loads using two sensor measurements

2019 ◽  
Vol 19 (3) ◽  
pp. 917-937 ◽  
Author(s):  
Zhenhua Nie ◽  
Jun Lin ◽  
Jun Li ◽  
Hong Hao ◽  
Hongwei Ma
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Structural Vibration ◽  
Measurement Information ◽  
Moving Mass ◽  
Moving Vehicle ◽  
Bridge Model ◽  
Vibration Responses ◽  
Beam Bridge

A novel damage detection approach using only two sensors to detect the damage in beam bridges subjected to a moving vehicle is proposed in this article. In this approach, a moving mass is considered representing a vehicle moving across the bridge, and structural vibration responses at two locations are measured from a pair of sensors. A moving window is defined with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses. The windowed pair time series extracted from these two measured responses are used to calculate the cross-correlation, which is used to define the local damage index. A simply supported beam bridge subjected to a moving mass is simulated to demonstrate the effectiveness and accuracy of the proposed approach. Numerical results indicate that the proposed approach can accurately identify the single and multiple damages using both displacement and acceleration responses, even when the responses are smeared with a significant noise. This indicates a good robustness to the noise effect. Experimental verifications on a laboratory beam bridge model demonstrate that the proposed approach can successfully identify the damage location using different selections of sensor pairs. Both the numerical and experimental results demonstrate that the new damage index is a good candidate for structural damage detection with very limited measurement information.

Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

2013 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Null Space ◽  
Singular Spectrum Analysis ◽  
Damage Index ◽  
Computation Efficiency ◽  
Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

FEM Free Damage Detection of Beam Structures Using the Deflections Estimated by Modal Flexibility Matrix

2021 ◽  
pp. 2150128
Author(s):  
Wen-Yu He ◽  
Wei-Xin Ren ◽  
Lei Cao ◽  
Quan Wang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Element Model ◽  
Mode Shapes ◽  
Beam Structures ◽  
Flexibility Matrix ◽  
Damage Quantification ◽  
Modal Flexibility ◽  
The Cost

The deflection of the beam estimated from modal flexibility matrix (MFM) indirectly is used in structural damage detection due to the fact that deflection is less sensitive to experimental noise than the element in MFM. However, the requirement for mass-normalized mode shapes (MMSs) with a high spatial resolution and the difficulty in damage quantification restricts the practicability of MFM-based deflection damage detection. A damage detection method using the deflections estimated from MFM is proposed for beam structures. The MMSs of beams are identified by using a parked vehicle. The MFM is then formulated to estimate the positive-bending-inspection-load (PBIL) caused deflection. The change of deflection curvature (CDC) is defined as a damage index to localize damage. The relationship between the damage severity and the deflection curvatures is further investigated and a damage quantification approach is proposed accordingly. Numerical and experimental examples indicated that the presented approach can detect damages with adequate accuracy at the cost of limited number of sensors. No finite element model (FEM) is required during the whole detection process.

Nonlinear Structural Damage Detection Based on Adaptive Volterra Filter Model

2018 ◽  
Vol 18 (02) ◽  
pp. 1871003 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Linear Models ◽  
Time History ◽  
Diagnostic Techniques ◽  
Filter Model ◽  
Volterra Filter ◽  
Damage Indices ◽  
Before And After ◽  
Nonlinear Time History

The majority of the existing damage diagnostic techniques are based on linear models. Changes in the state of the dynamics of these models, before and after damage in the structure based on the vibration measurements, are popularly used as damage indicators. However, the system may initially behave linearly and subsequently exhibit nonlinearity due to the incipience of damage. Breathing cracks that exhibit bilinear behavior are one such example of the damage induced due to nonlinearity. Further many real world structures even in their undamaged state are nonlinear. Hence, in this paper, we present a nonlinear damage detection technique based on the adaptive Volterra filter using the nonlinear time history response. Three damage indices based on the adaptive Volterra filter are proposed and their sensitiveness to damage and noise is assessed through two numerically simulated examples. Numerical investigations demonstrate the effectiveness of the adaptive Volterra filter model to detect damage in nonlinear structures even with measurement noise.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

Intelligent Systems for Structural Damage Assessment

2018 ◽  
Vol 29 (1) ◽  
pp. 378-392
Author(s):  
Eleni Vrochidou ◽  
Petros-Fotios Alvanitopoulos ◽  
Ioannis Andreadis ◽  
Anaxagoras Elenas
Keyword(s):  
Damage Assessment ◽  
Intelligent Systems ◽  
Structural Damage ◽  
Fuzzy Inference ◽  
Damage Index ◽  
Reinforced Concrete Structure ◽  
Ann Model ◽  
Inference System ◽  
Time Frequency ◽  
Artificial Neural Network Ann

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

Structural Health Monitoring Experimental System Implemented in a Turboprop Commercial Aircraft

2007 ◽  
Vol 347 ◽  
pp. 311-317
Author(s):  
Igor Bovio ◽  
Leonardo Lecce
Keyword(s):  
Structural Damage ◽  
Damage Index ◽  
Experimental System ◽  
Experimental Tests ◽  
Processing Technique ◽  
Commercial Aircraft ◽  
Destructive Testing ◽  
Before And After ◽  
Neural Network Algorithm ◽  
Testing Field

The purpose of the paper is to present an innovative application within the Non Destructive Testing field based upon vibration measurements developed by the authors, and already tested for analysing damage of many structural elements. After having tested this application on different test articles in laboratory condition, experimental tests have been executed, in collaboration with the ATR company, on a turboprop ATR-72 aircraft, in order to validate the technique on a real aeronautical structure. The monitoring system have operated an off-line check on the structure, during the aircraft ground operations, as if it were a normal maintenance procedure. The results are reported in the paper. This proposed new method is based upon the acquisition and comparison of the Frequency Response Functions (FRFs) of the monitored structure before and after damage occurs. Structural damage modify the dynamic behaviour of a structure affecting its mass, stiffness and damping, and consequently the FRFs of a damaged structure, when compared with the FRFs of its sound configuration, making the identification, localization and quantification of damage possible. The activities presented in the paper focus mainly on a new FRFs processing technique based upon the determination of a representative “Damage Index” for identifying and analysing damage. Furthermore, a dedicated neural network algorithm has been elaborated to develop an automatic system which recognises positive samples, “healthy” states of the analysed structure, discarding negative ones, “damaged or perturbed” states of the analysed structure. From an architectural standpoint, piezoceramic patches have been used as both actuators and sensors.

Damage Detection of Surface Crack in Composite Quasi-Isotropic Laminate Using Modal Analysis and Strain Energy Method

2006 ◽  
Vol 306-308 ◽  
pp. 757-762 ◽  
Author(s):  
Hui Wen Hu ◽  
Bor Tsuen Wang ◽  
Cheng Hsin Lee
Keyword(s):  
Damage Detection ◽  
Modal Analysis ◽  
Surface Crack ◽  
Strain Energy ◽  
Differential Quadrature Method ◽  
Damage Index ◽  
Mode Shapes ◽  
Cutting Machine ◽  
Before And After ◽  
Energy Formula

This paper presents a damage detection of surface crack in composite laminate. Carbon/epoxy composite AS4/PEEK was used to fabricate a quasi-isotropic laminate [0/90/±45]2s. Surface crack was created by using laser cutting machine. Modal analysis was performed to obtain the mode shapes of the laminate before and after damage. The mode shapes were then adopted to compute the strain energy, which was used to define a damage index. Consequently, the damage index successfully predicted the location of surface crack in the laminate. Differential quadrature method (DQM) was introduced to calculate the partial differential terms in strain energy formula.

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