Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery
Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the...
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Hindawi Limited
2009-01-01
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Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.3233/SAV-2009-0457 |
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doaj-6cdf0a61dad541aaa749a97035fad28a2020-11-25T00:49:09ZengHindawi LimitedShock and Vibration1070-96221875-92032009-01-01161899810.3233/SAV-2009-0457Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating MachineryJunsheng Cheng0Dejie Yu1Jiashi Tang2Yu Yang3The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, ChinaThe State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, ChinaCollege of Mechanics and Aerospace, Hunan University, Changsha, 410082, ChinaThe State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, ChinaTargeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.http://dx.doi.org/10.3233/SAV-2009-0457 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junsheng Cheng Dejie Yu Jiashi Tang Yu Yang |
spellingShingle |
Junsheng Cheng Dejie Yu Jiashi Tang Yu Yang Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery Shock and Vibration |
author_facet |
Junsheng Cheng Dejie Yu Jiashi Tang Yu Yang |
author_sort |
Junsheng Cheng |
title |
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery |
title_short |
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery |
title_full |
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery |
title_fullStr |
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery |
title_full_unstemmed |
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery |
title_sort |
application of svm and svd technique based on emd to the fault diagnosis of the rotating machinery |
publisher |
Hindawi Limited |
series |
Shock and Vibration |
issn |
1070-9622 1875-9203 |
publishDate |
2009-01-01 |
description |
Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small. |
url |
http://dx.doi.org/10.3233/SAV-2009-0457 |
work_keys_str_mv |
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