Aging state detection of viscoelastic sandwich structure using redundant second generation wavelet packet transform and fuzzy support vector data description

2021 ◽  
pp. 147592172110575
Author(s):  
Jinxiu Qu ◽  
Changquan Shi ◽  
Jinzhu Guo ◽  
Xiaowei Shi ◽  
Jiaqi Huang ◽  
...  
Keyword(s):  
Sandwich Structure ◽  
Wavelet Packet ◽  
Vibration Response ◽  
Structural Vibration ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Mechanical Equipment ◽  
Response Signal ◽  
State Detection ◽  
Second Generation Wavelet

Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.

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.

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

2019 ◽  
Vol 19 (3) ◽  
pp. 873-884
Author(s):  
Yue Si ◽  
Zhousuo Zhang ◽  
Chuiqing Kong ◽  
Shujuan Li ◽  
Guigeng Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sandwich Structure ◽  
Detection Method ◽  
Sandwich Structures ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Response ◽  
Permutation Entropy ◽  
Response Signal ◽  
Complex Wavelet

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

scholarly journals Research on Identification Method of Scour Depth for Bridge Based on ERA and SVM

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaozhong Zhang ◽  
Wenjuan Yao ◽  
Yimin Liu ◽  
Bo Chen
Keyword(s):  
Support Vector Machine ◽  
Vibration Response ◽  
Support Vector ◽  
Scour Depth ◽  
Bridge Scour ◽  
Response Signal ◽  
Machine Method ◽  
Random Decrement Technique ◽  
Identification Method ◽  
Ambient Excitation

A new damage identification method for bridge scour was proposed, in the case that it was difficult to detect bridge scour depth applying testing equipment. Through integrative application of the eigensystem realization algorithm (ERA) and method of support vector machine (SVM), this method was used to identify the scour depths of bridge under conditions of ambient excitation. The following three steps are necessary for the application of this method to identify bridge scour. Firstly, a sample library about scour depth and upper structure vibration response was established using numerical methods and support vector machine method. Secondly, free response signal of bridge were extracted from random vibration signal of bridge upper structure using random decrement technique. Thirdly, based on above two steps, the bridge scour depth was identified using ERA and SVM. In the process of applying the method to identify bridge scour depth, the key is to find the sensitive points for scour depth of substructure using sample library and to gather the vibration response signal of accelerated velocity under conditions of ambient excitation. It was identified that the method has higher recognition accuracy and better robustness through experiments on a real bridge. The method provided a new way for identifying scour depth of bridges.

scholarly journals Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianmin Zhou ◽  
Huijuan Guo ◽  
Long Zhang ◽  
Qingyao Xu ◽  
Hui Li
Keyword(s):  
Wavelet Packet ◽  
Assessment Method ◽  
Performance Degradation ◽  
Data Repository ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Mode Decomposition ◽  
Lifting Wavelet ◽  
Envelope Spectrum

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

Structure Health Monitoring Using Support Vector Data Description and Wavelet Packet Energy Distributions

2011 ◽  
Vol 135-136 ◽  
pp. 930-937
Author(s):  
Chen Dong Duan ◽  
Yi Yan Liu ◽  
Qiang Gao
Keyword(s):  
Health Monitoring ◽  
Wavelet Packet ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Distributions ◽  
Data Description ◽  
Structural Health ◽  
Ideal Candidate ◽  
Monitoring And Diagnostics

A new monitoring and diagnostics method using support vector data description (SVDD) is proposed which only needs samples under healthy condition. The method is an ideal candidate for coping with the problem of a shortage of the unhealthy condition samples. We firstly select several nodes of the monitored structure, and decompose the signals from these nodes with wavelet packet transform (WPT). To monitoring structural health efficiently, we assemble a combine feature by using wavelet packet energy distributions of these nodes. The feature is then applied as the input of a developed SVDD classifier. Experiment shows that the SVDD classifier was able to distinguish the normal and abnormal condition ideally, and can be used as an automation approach for structural health monitoring.

Sign in / Sign up

Export Citation Format

Share Document