Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM
In view of problems of difficult extracting of fault feature vector and unsatisfactory multi-classification effect of shearer rolling bearing, a fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM was proposed. The bearing fault signal is denoised by wavelet and decomposed by em...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2018-03-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017110006 |
| _version_ | 1797868896761413632 |
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| author | SUN Mingbo MA Qiuli ZHANG Yanliang LEI Junhui |
| author_facet | SUN Mingbo MA Qiuli ZHANG Yanliang LEI Junhui |
| author_sort | SUN Mingbo |
| collection | DOAJ |
| description | In view of problems of difficult extracting of fault feature vector and unsatisfactory multi-classification effect of shearer rolling bearing, a fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM was proposed. The bearing fault signal is denoised by wavelet and decomposed by empirical mode decomposition algorithm, then energy characteristic value is extracted and used as training set and test set of MSVM. The MSVM is used to identify fault status and parameters of MSVM are optimized by HGWO algorithm. The experimental results show that the fault diagnosis model of shearer bearing based on HGWO-MSVM can obviously improve accuracy and efficiency of fault identification compared with GWO, GA and PSO optimization MSVM model. |
| first_indexed | 2024-04-10T00:04:06Z |
| format | Article |
| id | doaj.art-13c8ad84c41d4d82b3a28fcdbd7a44d4 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:06Z |
| publishDate | 2018-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-13c8ad84c41d4d82b3a28fcdbd7a44d42023-03-17T01:20:01ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-03-01443818610.13272/j.issn.1671-251x.2017110006Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVMSUN MingboMA QiuliZHANG YanliangLEI JunhuiIn view of problems of difficult extracting of fault feature vector and unsatisfactory multi-classification effect of shearer rolling bearing, a fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM was proposed. The bearing fault signal is denoised by wavelet and decomposed by empirical mode decomposition algorithm, then energy characteristic value is extracted and used as training set and test set of MSVM. The MSVM is used to identify fault status and parameters of MSVM are optimized by HGWO algorithm. The experimental results show that the fault diagnosis model of shearer bearing based on HGWO-MSVM can obviously improve accuracy and efficiency of fault identification compared with GWO, GA and PSO optimization MSVM model.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017110006coal miningshearerrolling bearingfault diagnosisempirical mode decompositionhybrid grey wolf optimization algorithmmulti-class support vector machine |
| spellingShingle | SUN Mingbo MA Qiuli ZHANG Yanliang LEI Junhui Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM Gong-kuang zidonghua coal mining shearer rolling bearing fault diagnosis empirical mode decomposition hybrid grey wolf optimization algorithm multi-class support vector machine |
| title | Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM |
| title_full | Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM |
| title_fullStr | Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM |
| title_full_unstemmed | Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM |
| title_short | Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM |
| title_sort | fault diagnosis method for rolling bearing of shearer based on hgwo msvm |
| topic | coal mining shearer rolling bearing fault diagnosis empirical mode decomposition hybrid grey wolf optimization algorithm multi-class support vector machine |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2017110006 |
| work_keys_str_mv | AT sunmingbo faultdiagnosismethodforrollingbearingofshearerbasedonhgwomsvm AT maqiuli faultdiagnosismethodforrollingbearingofshearerbasedonhgwomsvm AT zhangyanliang faultdiagnosismethodforrollingbearingofshearerbasedonhgwomsvm AT leijunhui faultdiagnosismethodforrollingbearingofshearerbasedonhgwomsvm |