ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA
For the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2016-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.05.04 |
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| _version_ | 1850093483920982016 |
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| author | CHEN ChongYang XIONG BangShu HUANG JianPing MO Yan LI XinMin |
| author_facet | CHEN ChongYang XIONG BangShu HUANG JianPing MO Yan LI XinMin |
| author_sort | CHEN ChongYang |
| collection | DOAJ |
| description | For the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series of production functions（ PF） by the LMD method.Secondly,the estimate of PF was obtained after the PF components had been separated by ICA method,and the noise was effectively removed. Then,mutual information,correlation coefficient and approximate entropy which were extracted from the estimate of PF components were grouped together as a feature vector. Finally,the fault feature vectors were classified by SVM.The results of the feature extraction and fault diagnosis experiments show that the fault recognition rate of LMD-ICA method is significantly better than the traditional LMD method. |
| format | Article |
| id | doaj-art-0e1de21a9b2e4e329cec1d01ccc32073 |
| institution | DOAJ |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-0e1de21a9b2e4e329cec1d01ccc320732025-08-20T02:41:55ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692016-01-013892292630596335ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICACHEN ChongYangXIONG BangShuHUANG JianPingMO YanLI XinMinFor the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series of production functions（ PF） by the LMD method.Secondly,the estimate of PF was obtained after the PF components had been separated by ICA method,and the noise was effectively removed. Then,mutual information,correlation coefficient and approximate entropy which were extracted from the estimate of PF components were grouped together as a feature vector. Finally,the fault feature vectors were classified by SVM.The results of the feature extraction and fault diagnosis experiments show that the fault recognition rate of LMD-ICA method is significantly better than the traditional LMD method.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.05.04Rolling bearingLMDICASVM |
| spellingShingle | CHEN ChongYang XIONG BangShu HUANG JianPing MO Yan LI XinMin ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA Jixie qiangdu Rolling bearing LMD ICA SVM |
| title | ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA |
| title_full | ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA |
| title_fullStr | ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA |
| title_full_unstemmed | ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA |
| title_short | ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA |
| title_sort | rolling bearing fault diagnosis based on lmd and ica |
| topic | Rolling bearing LMD ICA SVM |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.05.04 |
| work_keys_str_mv | AT chenchongyang rollingbearingfaultdiagnosisbasedonlmdandica AT xiongbangshu rollingbearingfaultdiagnosisbasedonlmdandica AT huangjianping rollingbearingfaultdiagnosisbasedonlmdandica AT moyan rollingbearingfaultdiagnosisbasedonlmdandica AT lixinmin rollingbearingfaultdiagnosisbasedonlmdandica |