Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network
In order to realize the intelligent diagnosis of rolling bearing failure,a fault diagnosis model of vibration signal based on empirical mode decomposition（ EMD） and deep belief network（ DBN） is proposed.Firstly,the vibration signal is processed by empirical mode decomposition,and the statistical par...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.033 |
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| _version_ | 1841547391395692544 |
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| author | Yu Xiao Fan Chunyang Dong Fei Ding Enjie Wu Shoupeng Wang Xin |
| author_facet | Yu Xiao Fan Chunyang Dong Fei Ding Enjie Wu Shoupeng Wang Xin |
| author_sort | Yu Xiao |
| collection | DOAJ |
| description | In order to realize the intelligent diagnosis of rolling bearing failure,a fault diagnosis model of vibration signal based on empirical mode decomposition（ EMD） and deep belief network（ DBN） is proposed.Firstly,the vibration signal is processed by empirical mode decomposition,and the statistical parameters of the effective intrinsic modal function（ IMF） component and its Hilbert envelope are obtained as the original feature set. Then,the extreme learning suite（ ELM） classifier feature selection method is proposed to remove the redundancy and interference characteristics of the original feature set and to select the fault state sensitive features. Finally,by using the depth learning advantages in high-dimensional and non-linear processing,the DBN-based fault feature adaptive analysis and fault state intelligent identification is completed. The results show that the ELM method can select the sensitive statistical characteristics of the fault,and the adaptive characteristic of the DBN method can effectively improve the accuracy of fault state recognition. |
| format | Article |
| id | doaj-art-1b747350ed374e9eb0b276add6e20681 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-1b747350ed374e9eb0b276add6e206812025-01-10T14:42:00ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014215716329937238Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief NetworkYu XiaoFan ChunyangDong FeiDing EnjieWu ShoupengWang XinIn order to realize the intelligent diagnosis of rolling bearing failure,a fault diagnosis model of vibration signal based on empirical mode decomposition（ EMD） and deep belief network（ DBN） is proposed.Firstly,the vibration signal is processed by empirical mode decomposition,and the statistical parameters of the effective intrinsic modal function（ IMF） component and its Hilbert envelope are obtained as the original feature set. Then,the extreme learning suite（ ELM） classifier feature selection method is proposed to remove the redundancy and interference characteristics of the original feature set and to select the fault state sensitive features. Finally,by using the depth learning advantages in high-dimensional and non-linear processing,the DBN-based fault feature adaptive analysis and fault state intelligent identification is completed. The results show that the ELM method can select the sensitive statistical characteristics of the fault,and the adaptive characteristic of the DBN method can effectively improve the accuracy of fault state recognition.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.033Empirical modal decompositionExtreme learning machineDeep belief networkRolling bearingFault diagnosis |
| spellingShingle | Yu Xiao Fan Chunyang Dong Fei Ding Enjie Wu Shoupeng Wang Xin Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network Jixie chuandong Empirical modal decomposition Extreme learning machine Deep belief network Rolling bearing Fault diagnosis |
| title | Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network |
| title_full | Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network |
| title_fullStr | Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network |
| title_full_unstemmed | Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network |
| title_short | Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network |
| title_sort | fault feature analysis and diagnosis method of rolling bearing based on empirical mode decomposition and deep belief network |
| topic | Empirical modal decomposition Extreme learning machine Deep belief network Rolling bearing Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.033 |
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