Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM
Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/1308108 |
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| _version_ | 1832565007740567552 |
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| author | Jiang Xingmeng Wu Li Pan Liwu Ge Mingtao Hu Daidi |
| author_facet | Jiang Xingmeng Wu Li Pan Liwu Ge Mingtao Hu Daidi |
| author_sort | Jiang Xingmeng |
| collection | DOAJ |
| description | Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method. |
| format | Article |
| id | doaj-art-1ad9795574b34f3cb08f9fc9379551b5 |
| institution | Kabale University |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-1ad9795574b34f3cb08f9fc9379551b52025-02-03T01:09:34ZengWileyJournal of Engineering2314-49042314-49122016-01-01201610.1155/2016/13081081308108Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVMJiang Xingmeng0Wu Li1Pan Liwu2Ge Mingtao3Hu Daidi4Zhengzhou Railway Vocational & Technical College, No. 9 Qiancheng Road, Zhengdong New District, Zhengzhou, Henan 451460, ChinaCollege of Electronics and Information Engineering, SIAS International University, No. 168 Renmin Road, Xinzheng 451150, ChinaDepartment of Automation & Control, Henan University of Animal Husbandry & Economy, Zhengzhou, Henan 451150, ChinaCollege of Electronics and Information Engineering, SIAS International University, No. 168 Renmin Road, Xinzheng 451150, ChinaCollege of Electronics and Information Engineering, SIAS International University, No. 168 Renmin Road, Xinzheng 451150, ChinaAiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.http://dx.doi.org/10.1155/2016/1308108 |
| spellingShingle | Jiang Xingmeng Wu Li Pan Liwu Ge Mingtao Hu Daidi Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM Journal of Engineering |
| title | Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM |
| title_full | Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM |
| title_short | Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM |
| title_sort | rolling bearing fault diagnosis based on elcd permutation entropy and rvm |
| url | http://dx.doi.org/10.1155/2016/1308108 |
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