Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing
Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local...
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Hindawi Limited
2015-01-01
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Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2015/847802 |
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doaj-38ff46c5207b4ea7b021aac97877d4e32020-11-24T23:14:52ZengHindawi LimitedShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/847802847802Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller BearingSongrong Luo0Junsheng Cheng1HungLinh Ao2College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415003, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaInstitute for Computational Science, Ton Duc Thang University, Ho Chi Minh 70000, VietnamTargeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.http://dx.doi.org/10.1155/2015/847802 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Songrong Luo Junsheng Cheng HungLinh Ao |
spellingShingle |
Songrong Luo Junsheng Cheng HungLinh Ao Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing Shock and Vibration |
author_facet |
Songrong Luo Junsheng Cheng HungLinh Ao |
author_sort |
Songrong Luo |
title |
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing |
title_short |
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing |
title_full |
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing |
title_fullStr |
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing |
title_full_unstemmed |
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing |
title_sort |
application of lcd-svd technique and cro-svm method to fault diagnosis for roller bearing |
publisher |
Hindawi Limited |
series |
Shock and Vibration |
issn |
1070-9622 1875-9203 |
publishDate |
2015-01-01 |
description |
Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods. |
url |
http://dx.doi.org/10.1155/2015/847802 |
work_keys_str_mv |
AT songrongluo applicationoflcdsvdtechniqueandcrosvmmethodtofaultdiagnosisforrollerbearing AT junshengcheng applicationoflcdsvdtechniqueandcrosvmmethodtofaultdiagnosisforrollerbearing AT hunglinhao applicationoflcdsvdtechniqueandcrosvmmethodtofaultdiagnosisforrollerbearing |
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1725592989321920512 |