scholarly journals Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing

2016 ◽  
Vol 368 ◽  
pp. 249-263 ◽  
Author(s):  
Chuang Sun ◽  
Zhousuo Zhang ◽  
Xue Luo ◽  
Ting Guo ◽  
Jinxiu Qu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Health Monitoring ◽  
Sandwich Structure ◽  
Grassmann Manifold ◽  
Support Vector ◽  
Material Ageing

A novel aging state recognition method of a viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine

2019 ◽  
Vol 19 (1) ◽  
pp. 156-172 ◽  
Author(s):  
Jinxiu Qu ◽  
Changquan Shi ◽  
Feng Ding ◽  
Wenjuan Wang
Keyword(s):  
Support Vector Machine ◽  
Sandwich Structure ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Response ◽  
Permutation Entropy ◽  
Support Vector ◽  
State Recognition ◽  
Complex Wavelet ◽  
Viscoelastic Layers

A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.

Structural Health Monitoring of Truss Type Structures Using Statistical Approach

2014 ◽  
Author(s):  
Mahdi Saffari ◽  
Ramin Sedaghati ◽  
Ion Stiharu
Keyword(s):  
Support Vector Machine ◽  
Health Monitoring ◽  
Damage Index ◽  
Crack Size ◽  
Baseline Data ◽  
Support Vector ◽  
Feature Vectors ◽  
Space Truss ◽  
Damage Data ◽  
Space Truss Structure

This paper proposes an effective statistical based vibration health monitoring technique using Auto Regressive (AR) parameters and Support Vector Machine (SVM) for truss type structures. The finite element method has been utilized to obtain acceleration response signals of a space truss structure under random excitations. The signals are then processed to extract their AR parameters as the feature vectors in which the AR parameters of the healthy structure are considered to be the reference baseline data. A Damage Index is then defined to be the standard deviation of the feature vectors from the baseline data. The proposed index provides an effective tool to detect the damage in the structure. It is shown that using only one sensor, it is still possible to accurately detect the damage. To locate the damage, data classification technique based on Support Vector Machine (SVM) has been employed. It is shown that SVM can successfully classify different signals extracted from the structure. Finally extensive sensitivity analysis has been performed to study the effect of different parameter such as crack size, number of sensors and AR parameter numbers on the accuracy of detection and localization processes.

Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1365-1371 ◽  
Author(s):  
Jian Hong Xie
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Least Square ◽  
Support Vector ◽  
Structural Damage Detection ◽  
Quantum Genetic Algorithm

Structural damage detection and health monitoring is very important in many applications, and a key related issue is the method of damage detection. In this paper, Fuzzy Least Square Support Vector Machine (FLS-SVM) is constructed by combining Fuzzy Logic with LS-SVM, and a real-coded Quantum Genetic Algorithm (QGA) is applied to optimize parameters of FLS-SVM. Then, the method of FLS-SVM integrated QGA is used to detect damages for fiber smart structures. The testing results show FLS-SVM possesses the higher detecting accuracy and the bitter dissemination ability than LS-SVM under the same conditions, and the parameters of FLS-SVM can be effectively optimized by the real-coded QGA. The proposed method of FLS-SVM integrated QGA is effective and efficient for structural damage detection.

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