scholarly journals Damage Detection Based on Cross-Term Extraction from Bilinear Time-Frequency Distributions

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ma Yuchao ◽  
Yan Weiming ◽  
He Haoxiang ◽  
Wang Kai
Keyword(s):  
Damage Detection ◽  
Frequency Distribution ◽  
Structural Damage ◽  
Damage Identification ◽  
Dominant Term ◽  
Cross Term ◽  
Time Frequency ◽  
Structural Damage Identification ◽  
The Cross ◽  
Time Frequency Distribution

Abundant damage information is implicated in the bilinear time-frequency distribution of structural dynamic signals, which could provide effective support for structural damage identification. Signal time-frequency analysis methods are reviewed, and the characters of linear time-frequency distribution and bilinear time-frequency distribution typically represented by the Wigner-Ville distribution are compared. The existence of the cross-term and its application in structural damage detection are demonstrated. A method of extracting the dominant term is proposed, which combines the short-time Fourier spectrum and Wigner-Ville distribution; then two-dimensional time-frequency transformation matrix is constructed and the complete cross-term is extracted finally. The distribution character of which could be applied to the structural damage identification. Through theoretical analysis, model experiment and numerical simulation of the girder structure, the change rate of cross-term amplitude is validated to identify the damage location and degree. The effectiveness of the cross-term of bilinear time-frequency distribution for damage detection is confirmed and the analytical method of damage identification used in structural engineering is available.

scholarly journals A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Jiexiao Yu ◽  
Kaihua Liu ◽  
Liang Zhang ◽  
Peng Luo
Keyword(s):  
Frequency Distribution ◽  
Filter Design ◽  
Wigner Distribution ◽  
Piecewise Linear ◽  
Support Vector ◽  
Svm Classifier ◽  
Cross Term ◽  
Time Frequency ◽  
Fractional Filter ◽  
Time Frequency Distribution

The second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term. A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. After that, the parameters and transfer function of filter can be determined by the parameters of the separating lines directly in the case of linear separability or line segments after the piecewise linear fitting of the separating curves in the case of nonlinear separability. Without any prior knowledge of signal and noise, this method can meet the reliability and universality simultaneously for filter design and realize the global optimization of filter parameters by machine learning even in the case of strong coupling between signal and noise. Furthermore, it could completely eliminate the cross-term in Wigner-Ville distribution (WVD) and the time-frequency distribution we get in the end has high resolution and good readability even when autoterms and cross-terms overlap. Simulation results verified the efficiency of this method.

Structural Damage Detection Using Curvature Mode Difference Curve

2011 ◽  
Vol 250-253 ◽  
pp. 1248-1251 ◽  
Author(s):  
Hang Jing ◽  
Ling Ling Jia ◽  
Yi Zhao
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Numerical Differentiation ◽  
Engineering Structures ◽  
System Parameters ◽  
Numerical Studies ◽  
Damage Indicator ◽  
Structural Damage Identification ◽  
Changing Rate

Damage detection in civil engineering structures using the dynamic system parameters has become an important area of research. The sensitivity of damage indicator is of great value to structural damage identification. The curvature mode is an excellent parameter in damage detection of structures, while in case that certain curvature mode curve can’t show existence of damage. In this paper, numerical studies are conducted to demonstrate the deficiency of curvature mode to damage detection. Then a new damage indicator called “curvature mode changing rate” (CMCR) is introduced which is processed by numerical differentiation of curvature mode curve. The simulation results show that the new index is superior to curvature mode for structural damage identification, and it is still sensitive to the damaged location in the mode node.

Structural Damage Identification Method Based On Transmissibility in Time Domain

2021 ◽  
Author(s):  
Yunfeng Zou ◽  
Xuandong Lu ◽  
Jinsong Yang ◽  
Xuhui He ◽  
Tiantian Wang
Keyword(s):  
Time Domain ◽  
Structural Damage ◽  
High Efficiency ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Reconstruction Method ◽  
Fe Model ◽  
Time Frequency ◽  
Identification Method ◽  
Structural Damage Identification

Abstract Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novelty structural damage identification method based on the transmissibility in time domain is proposed. The method takes the discrepancy of transmissibility of structure response in time domain before and after damage as the basis of finite element model modification. The damage location and damage degree are obtained through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by FE model. Taking the advantage of the response reconstruction method based on empirical mode decomposition, the damage information is possible to obtain in the absence of prior knowledge on external excitation information. Moreover, this method is carried out in the time domain, without the need to identify the modal parameters and perform time-frequency analysis, which simplicity ensures the high efficiency of damage identification. The effectiveness and accuracy of the proposed method are studied by simulation, including reconstruction error and measurement noise. The identification results demonstrate that the proposed structural damage identification method improves the calculation effectiveness considerably and ensures the identification accuracy.

scholarly journals Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

2021 ◽  
Vol 11 (6) ◽  
pp. 2610
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Soojin Jang ◽  
Kyohoon Jin ◽  
Youngbin Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Input Data ◽  
Damage Identification ◽  
Beam Model ◽  
Data Normalization ◽  
Structural Damage Detection ◽  
Structural Damage Identification

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

Sign in / Sign up

Export Citation Format

Share Document