MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
Bearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/921 |
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| author | Guangyuan Xu Ruifeng Guo Zhenyu Yin Feiqing Zhang |
| author_facet | Guangyuan Xu Ruifeng Guo Zhenyu Yin Feiqing Zhang |
| author_sort | Guangyuan Xu |
| collection | DOAJ |
| description | Bearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer learning. Firstly, this paper designs a feature extraction network, the Multi-scale Convolution-Convolutional Reconstruction Network (MCRCNet), which incorporates a multi-scale feature extraction module to extract bearing fault features at multiple scales, thereby enhancing the extraction ability of key information. Secondly, this paper designs an improved convolutional reconstruction module AcConv (Adaptive Convolution reconstruction), which highlights key feature information and reduces redundant features by reconstructing the feature map. Furthermore, this paper also modifies the loss function to improve the performance in the case of data imbalance, and introduces the Wasserstein distance to optimize the adversarial training process. The proposed method is experimentally verified on Case Western Reserve University, Jiangnan University, and laboratory datasets. The experimental results show that the method has good performance in most tasks and has good generalization ability, which provides a feasible solution for the research of bearing fault diagnosis. |
| format | Article |
| id | doaj-art-5b5b6a41518e43c1a6ebfd1ad8709e9f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5b5b6a41518e43c1a6ebfd1ad8709e9f2025-01-24T13:21:21ZengMDPI AGApplied Sciences2076-34172025-01-0115292110.3390/app15020921MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer LearningGuangyuan Xu0Ruifeng Guo1Zhenyu Yin2Feiqing Zhang3Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaBearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer learning. Firstly, this paper designs a feature extraction network, the Multi-scale Convolution-Convolutional Reconstruction Network (MCRCNet), which incorporates a multi-scale feature extraction module to extract bearing fault features at multiple scales, thereby enhancing the extraction ability of key information. Secondly, this paper designs an improved convolutional reconstruction module AcConv (Adaptive Convolution reconstruction), which highlights key feature information and reduces redundant features by reconstructing the feature map. Furthermore, this paper also modifies the loss function to improve the performance in the case of data imbalance, and introduces the Wasserstein distance to optimize the adversarial training process. The proposed method is experimentally verified on Case Western Reserve University, Jiangnan University, and laboratory datasets. The experimental results show that the method has good performance in most tasks and has good generalization ability, which provides a feasible solution for the research of bearing fault diagnosis.https://www.mdpi.com/2076-3417/15/2/921fault diagnosistransfer learningconvolutional reconstructionloss functionWasserstein distance |
| spellingShingle | Guangyuan Xu Ruifeng Guo Zhenyu Yin Feiqing Zhang MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning Applied Sciences fault diagnosis transfer learning convolutional reconstruction loss function Wasserstein distance |
| title | MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning |
| title_full | MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning |
| title_fullStr | MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning |
| title_full_unstemmed | MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning |
| title_short | MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning |
| title_sort | mcrcnet a bearing fault diagnosis method for unknown faults based on transfer learning |
| topic | fault diagnosis transfer learning convolutional reconstruction loss function Wasserstein distance |
| url | https://www.mdpi.com/2076-3417/15/2/921 |
| work_keys_str_mv | AT guangyuanxu mcrcnetabearingfaultdiagnosismethodforunknownfaultsbasedontransferlearning AT ruifengguo mcrcnetabearingfaultdiagnosismethodforunknownfaultsbasedontransferlearning AT zhenyuyin mcrcnetabearingfaultdiagnosismethodforunknownfaultsbasedontransferlearning AT feiqingzhang mcrcnetabearingfaultdiagnosismethodforunknownfaultsbasedontransferlearning |