STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD

How to extract characteristic parameters from vibration signals with noise is a key problem of bearing fault diagnosis. A novel method based on Morlet wavelet-Singular Value Decomposition( SVD) and Variable Predictive Model based Class Discriminate( VPMCD) was proposed in this paper aiming to solve...

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Main Authors: QI Peng, FAN YuGang, WU JianDe
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2017-01-01
Series:Jixie qiangdu
Subjects:
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.002
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author QI Peng
FAN YuGang
WU JianDe
author_facet QI Peng
FAN YuGang
WU JianDe
author_sort QI Peng
collection DOAJ
description How to extract characteristic parameters from vibration signals with noise is a key problem of bearing fault diagnosis. A novel method based on Morlet wavelet-Singular Value Decomposition( SVD) and Variable Predictive Model based Class Discriminate( VPMCD) was proposed in this paper aiming to solve this problem. Firstly,Morlet wavelet transform was used to pre-process the signals in the time domain to obtain a time-frequency coefficient matrix,then SVD was applied to the matrix to remove noise and extract the weak fault information in the corresponding dimensions according to the singular value curvature spectrum; Secondly,the signal components near the optimal meature were selected,and the Shannon energy entropy were used as the characteristic parameters to construct the feature vectors,which were then used to establish the fault identification model based on VPMCD. Finally,5-fold cross validation method and Jackknife test method were adopted to verify the proposed method,and the results have demonstrated its effectiveness.
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spelling doaj.art-34085f452b98482eba1ed83e97d318d52023-08-01T07:44:01ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692017-01-013924725330597889STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCDQI PengFAN YuGangWU JianDeHow to extract characteristic parameters from vibration signals with noise is a key problem of bearing fault diagnosis. A novel method based on Morlet wavelet-Singular Value Decomposition( SVD) and Variable Predictive Model based Class Discriminate( VPMCD) was proposed in this paper aiming to solve this problem. Firstly,Morlet wavelet transform was used to pre-process the signals in the time domain to obtain a time-frequency coefficient matrix,then SVD was applied to the matrix to remove noise and extract the weak fault information in the corresponding dimensions according to the singular value curvature spectrum; Secondly,the signal components near the optimal meature were selected,and the Shannon energy entropy were used as the characteristic parameters to construct the feature vectors,which were then used to establish the fault identification model based on VPMCD. Finally,5-fold cross validation method and Jackknife test method were adopted to verify the proposed method,and the results have demonstrated its effectiveness.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.002Singular value decomposition;Variable predictive model;Wavelet transform;Energy entropy;Fault diagnosis
spellingShingle QI Peng
FAN YuGang
WU JianDe
STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
Jixie qiangdu
Singular value decomposition;Variable predictive model;Wavelet transform;Energy entropy;Fault diagnosis
title STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
title_full STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
title_fullStr STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
title_full_unstemmed STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
title_short STUDY ON FAULT DIAGNOSIS METHOD BASED ON MORLET WAVELET-SVD AND VPMCD
title_sort study on fault diagnosis method based on morlet wavelet svd and vpmcd
topic Singular value decomposition;Variable predictive model;Wavelet transform;Energy entropy;Fault diagnosis
url http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.002
work_keys_str_mv AT qipeng studyonfaultdiagnosismethodbasedonmorletwaveletsvdandvpmcd
AT fanyugang studyonfaultdiagnosismethodbasedonmorletwaveletsvdandvpmcd
AT wujiande studyonfaultdiagnosismethodbasedonmorletwaveletsvdandvpmcd