Bearing Fault Diagnosis Method based on Sensitive Component and MCPG
Aiming at the problem that is difficult to accurately identify rolling bearing faults， a fault diagnosis method based on sensitive components and Multi Convolution Pooling Group （MCPG） is proposed. Firstly， the Empirical Mode Decomposition （EMD） is used to decompose the original signal into multiple...
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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
2021-04-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.014 |
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| _version_ | 1841547301728813056 |
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| author | Mingliang Zhang Hongkun Li Yue Ma Gangjin Huang Yuchen Xu |
| author_facet | Mingliang Zhang Hongkun Li Yue Ma Gangjin Huang Yuchen Xu |
| author_sort | Mingliang Zhang |
| collection | DOAJ |
| description | Aiming at the problem that is difficult to accurately identify rolling bearing faults， a fault diagnosis method based on sensitive components and Multi Convolution Pooling Group （MCPG） is proposed. Firstly， the Empirical Mode Decomposition （EMD） is used to decompose the original signal into multiple Intrinsic Mode Function（IMF）， and the discrete Fréchet distance is used as the measurement index， the fault sensitive components are selected as the fault data sources representing different fault types. Then， a MCPG deep neural network architecture is proposed， and sensitive data sources are used to train and test the model to achieve the data-driven bearing fault diagnosis. Through experimental verification， it is proved that the method has good recognition effect on different types of vibration data （different speeds， different damage types， different damage degrees）. |
| format | Article |
| id | doaj-art-eb1d2470976f447199820a5a2e3ee783 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-04-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-eb1d2470976f447199820a5a2e3ee7832025-01-10T14:49:20ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-04-014580878812996Bearing Fault Diagnosis Method based on Sensitive Component and MCPGMingliang ZhangHongkun LiYue MaGangjin HuangYuchen XuAiming at the problem that is difficult to accurately identify rolling bearing faults， a fault diagnosis method based on sensitive components and Multi Convolution Pooling Group （MCPG） is proposed. Firstly， the Empirical Mode Decomposition （EMD） is used to decompose the original signal into multiple Intrinsic Mode Function（IMF）， and the discrete Fréchet distance is used as the measurement index， the fault sensitive components are selected as the fault data sources representing different fault types. Then， a MCPG deep neural network architecture is proposed， and sensitive data sources are used to train and test the model to achieve the data-driven bearing fault diagnosis. Through experimental verification， it is proved that the method has good recognition effect on different types of vibration data （different speeds， different damage types， different damage degrees）.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.014Fault diagnosisRolling bearingEMDDiscrete FréchetConvolutional neural networks |
| spellingShingle | Mingliang Zhang Hongkun Li Yue Ma Gangjin Huang Yuchen Xu Bearing Fault Diagnosis Method based on Sensitive Component and MCPG Jixie chuandong Fault diagnosis Rolling bearing EMD Discrete Fréchet Convolutional neural networks |
| title | Bearing Fault Diagnosis Method based on Sensitive Component and MCPG |
| title_full | Bearing Fault Diagnosis Method based on Sensitive Component and MCPG |
| title_fullStr | Bearing Fault Diagnosis Method based on Sensitive Component and MCPG |
| title_full_unstemmed | Bearing Fault Diagnosis Method based on Sensitive Component and MCPG |
| title_short | Bearing Fault Diagnosis Method based on Sensitive Component and MCPG |
| title_sort | bearing fault diagnosis method based on sensitive component and mcpg |
| topic | Fault diagnosis Rolling bearing EMD Discrete Fréchet Convolutional neural networks |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.014 |
| work_keys_str_mv | AT mingliangzhang bearingfaultdiagnosismethodbasedonsensitivecomponentandmcpg AT hongkunli bearingfaultdiagnosismethodbasedonsensitivecomponentandmcpg AT yuema bearingfaultdiagnosismethodbasedonsensitivecomponentandmcpg AT gangjinhuang bearingfaultdiagnosismethodbasedonsensitivecomponentandmcpg AT yuchenxu bearingfaultdiagnosismethodbasedonsensitivecomponentandmcpg |