Wavelet-Based Sensitivity Analysis of the Impulse Response Function for Damage Detection

2006 ◽  
Vol 74 (2) ◽  
pp. 375-377 ◽  
Author(s):  
S. S. Law ◽  
X. Y. Li
Keyword(s):  
Damage Detection ◽  
Impulse Response ◽  
Response Function ◽  
Structural Damage ◽  
Impulse Response Function ◽  
Model Error ◽  
Discrete Wavelet ◽  
Structural Damage Detection ◽  
Acceleration Response ◽  
Damage Scenarios

This paper discusses the extraction of the wavelet-based impulse response function from the acceleration response using the discrete wavelet transform (DWT). The analytical formulation of the sensitivity of the DWT coefficient of the impulse response function with respect to a system parameter is then presented for structural damage detection. A numerical example with a 31-bar plane truss structure is used to verify the proposed method with different damage scenarios with or without model error and noise effect.

scholarly journals Structural Damage Diagnosis-Oriented Impulse Response Function Estimation under Seismic Excitations

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5413
Author(s):  
Jian-Fu Lin ◽  
Junfang Wang ◽  
Li-Xin Wang ◽  
Siu-seong Law
Keyword(s):  
Damage Detection ◽  
Impulse Response ◽  
Response Function ◽  
Structural Damage ◽  
Numerical Study ◽  
Damage Index ◽  
Impulse Response Function ◽  
Function Estimation ◽  
Seismic Excitations ◽  
Transformation Matrices

Impulse response function (IRF) is an ideal structural damage index for the identification of structural damage associated with changes in modal properties. However, IRFs from multiple excitations applied at different degrees-of-freedoms jointly contribute to the dynamic response, and their estimation is often underdetermined. Although some efforts have been devoted to the estimation of IRF for a structure under single excitation, the case under multiple excitations has not been fully investigated yet. The estimation of IRF under multiple excitations is generally an ill-conditioned inverse problem such that an incorrect or non-feasible solution is common, preventing its application to damage detection. This work explores this problem by introducing dimensionality reduction transformation matrices relating two sets of IRFs of a structure with discussions on the performance of the non-unique transformation matrices. Then, the extraction of IRF via wavelet-based and Tikhonov regularization-based methods are compared. Finally, a numerical study with a truss structure is conducted to validate the estimation of the IRFs and to demonstrate their applicability for damage detection under seismic excitations. Both the damage locations and severity are accurately identified, indicating the proposed methodology can enable the IRFs estimation under multiple excitations for successful damage detection.

Flexibility-Based Objective Functions for Constrained Optimization Problems on Structural Damage Detection

2011 ◽  
Vol 186 ◽  
pp. 383-387 ◽  
Author(s):  
Xi Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Constrained Optimization ◽  
Objective Function ◽  
Structural Damage ◽  
Optimization Problems ◽  
Objective Functions ◽  
Structural Damage Detection ◽  
Constrained Optimization Problems ◽  
Modal Assurance Criterion ◽  
Damage Scenarios

Based on concepts of structural modal flexibility and modal assurance criterion (MAC), a new objective function is defined and studied for constrained optimization problems (COP) on structural damage detection (SDD) in this paper. Compared with traditionally objective function, which is defined based on natural frequencies and MAC, effect of objective functions on robustness of SDD calculation is evaluated through numerical simulation of a 2-storey rigid frame. Structural damages are identified by solving the COP on SDD based on an improved particle swarm optimization (IPSO) algorithm. Weak and multiple damage scenarios are mainly considered in various noise conditions. Some illustrated results show that the newly defined objective function is better than the traditional ones. It can be used to identify the damage locations but also to quantify the severity of weak and multiple damages in measurement noise conditions.

Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

2020 ◽  
pp. 147592172093405
Author(s):  
Zilong Wang ◽  
Young-Jin Cha
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Training Data ◽  
Support Vector ◽  
Acceleration Response ◽  
Damage Scenarios ◽  
Response Data ◽  
Unsupervised Deep Learning

This article proposes an unsupervised deep learning–based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.

Structural Damage Detection Using Signal Pattern-Recognition

2008 ◽  
Vol 400-402 ◽  
pp. 465-470 ◽  
Author(s):  
Long Qiao ◽  
Asad Esmaeily ◽  
Hani G. Melhem
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Structural Damage ◽  
Experimental Studies ◽  
Least Square ◽  
Structural Damage Detection ◽  
Damage Scenario ◽  
Damage Pattern ◽  
Damage Scenarios ◽  
Unknown Damage

Deterioration significantly affects the structure performance and safety. A signal-based pattern-recognition procedure is applied for structural damage detection with a limited number of input/output signals. The method is based on extracting and selecting the sensitive features of the structure response to form a unique pattern for any particular damage scenario, and recognizing the unknown damage pattern against the known database to identify the damage location and level (severity). In this study, two types of transformation algorithms are implemented separately for feature extraction: (1) Continuous Wavelet Transform (CWT); and (2) Wavelet Packet Transform (WPT). Three pattern-matching algorithms are also implemented separately for pattern recognition: (1) correlation, (2) least square distance, and (3) Cosh spectral distance. To demonstrate the validity and accuracy of the procedure, experimental studies are conducted on a simple three-story steel structure. The results show that the features of the signal for different damage scenarios can be uniquely identified by these transformations, and correlation algorithms can best perform pattern recognition to identify the unknown damage pattern. The proposed method can also be used to possibly detect the type of damage. It is suitable for structural health monitoring, especially for online monitoring applications.

Structural damage detection in plates using a deep neural network–couple sparse coding classification ensemble method

2020 ◽  
pp. 107754632092915
Author(s):  
Vahid Bokaeian ◽  
Faramarz Khoshnoudian ◽  
Milad Fallahian
Keyword(s):  
Damage Detection ◽  
Frequency Response ◽  
Response Function ◽  
Sparse Coding ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Principal Component ◽  
Plate Structures ◽  
Feature Extraction Method ◽  
Damage Scenarios

The present study aims at identifying damages in plate structures by applying a pattern recognition–based damage detection technique using the frequency response function. The large number of degrees of freedom is one of the crucial obstacles in the way of accurately identifying damages in plate structures. On the other hand, frequency response functions include many details that dramatically lower the computing speed and enlarge the memory needed for storing data, hampering the application of this method. Furthermore, this study performs principal component analysis as an authoritative feature extraction method with the purpose of reducing the dimensions of the measured frequency response function data and generating distinct feature patterns. Also, because there has been no individual optimal classifier applicable to all problems, an ensemble comprising two powerful classifiers containing couple sparse coding classification and deep neural networks is used to predict the structure damage. This study evaluates the accuracy of damage detection by the proposed method in square-shaped structural plates with the lengths of 1 m and 2 m under different damage scenarios, namely, single and multiple element.

scholarly journals Structure Damage Identification Based on Regularized ARMA Time Series Model under Environmental Excitation

Vibration ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 138-156 ◽  
Author(s):  
Xuan Zhang ◽  
Dongsheng Li ◽  
Gangbing Song
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Response Function ◽  
Structural Damage ◽  
Damage Identification ◽  
National Laboratory ◽  
Impulse Response Function ◽  
Time Series Model ◽  
Vibration Response ◽  
Structural Vibration

In this paper, a non-modal parametric method to identify structural damage using a regularized autoregressive moving average time series model under environmental excitation is proposed in combination with the virtual impulse response function. This method can use the structural vibration response to determine the damage caused to the structure during environmental excitation. Firstly, the virtual impulse response function is obtained by using the structural vibration response. Then, a regularized ARMA time series model is used to fit the virtual impulse response function. Based on the change of auto-regression coefficients in the regularization model under different damage cases, the structural damage is identified. The authors derive the regularization equation of an ARMA time series model to solve the problems in a time series model and obtain the regularization coefficient. Finally, this method is applied to a three-degrees-of-freedom chain structure and a three-floor shear structure of the Los Alamos National Laboratory (LANL). The experimental results show that the method based on the regularized ARMA time series model under environmental excitation can effectively identify the structural damage, which is a reliable method for damage identification. The regularized ARMA time series model can accurately extract signal features and has invaluable application prospects in the field of structural health monitoring.

Unit Impulse Response Estimation for Structural Damage Detection Under Planar Multiple Excitations

2013 ◽  
Vol 81 (2) ◽  
Author(s):  
Siu-seong Law ◽  
Jian-fu Lin
Keyword(s):  
Damage Detection ◽  
Impulse Response ◽  
Structural Damage ◽  
Sampling Rate ◽  
Model Errors ◽  
Damage Scenarios ◽  
Unit Impulse ◽  
Duhamel Integral ◽  
The Stability ◽  
Unit Impulse Response

An unit impulse response (UIR) function is an inherent system function that depends only on the structure and the locations of excitation. When the structure is under general excitation, the effect can be obtained via Duhamel integral between the UIR function and the excitation. The possibility of the UIR function as a damage detection index under general excitation is studied here. However, the UIRs from multiple excitations will contribute to the identification equation together. Therefore, the estimation of UIRs will most probably become underdetermined with a limited number of measurements leading to an incorrect or nonfeasible solution of the inverse problem. This report addresses this problem by developing a transformation between the UIRs to facilitate the conversion of a multiple excitations problem into an equivalent single excitation problem. However, the method is limited to planar problems and the reference response is confined to those with larger vibration amplitude. The extraction of the UIR via Tikhonov regularization from the measured acceleration is then described. Numerical studies with a 31-bar plane truss structure are used to illustrate the performances of the proposed approach with different damage scenarios with or without noise effect and model errors. The stability of the UIR estimation with different periods of measured data is also studied. Moreover, this paper studies the effect of using a reduced number of sensors and an increased sampling rate. Results show that the regularization-based approach with the new transformation matrix is accurate and effective for the inverse solution and it is robust to measurement noise in the damage detection process.

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