Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/2/551 |
| _version_ | 1797340201506308096 |
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| author | Kai Wang Bo Gao Shijie Shan Rong Wang Xueyang Wang |
| author_facet | Kai Wang Bo Gao Shijie Shan Rong Wang Xueyang Wang |
| author_sort | Kai Wang |
| collection | DOAJ |
| description | Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets. |
| first_indexed | 2024-03-08T09:58:31Z |
| format | Article |
| id | doaj.art-2482cacb4c0f4980a1f5d318eb397d5c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:31Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2482cacb4c0f4980a1f5d318eb397d5c2024-01-29T13:42:39ZengMDPI AGApplied Sciences2076-34172024-01-0114255110.3390/app14020551Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANetKai Wang0Bo Gao1Shijie Shan2Rong Wang3Xueyang Wang4School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaMost fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets.https://www.mdpi.com/2076-3417/14/2/551fault diagnosisattention mechanismfeature fusionECA-MRANet |
| spellingShingle | Kai Wang Bo Gao Shijie Shan Rong Wang Xueyang Wang Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet Applied Sciences fault diagnosis attention mechanism feature fusion ECA-MRANet |
| title | Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet |
| title_full | Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet |
| title_fullStr | Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet |
| title_full_unstemmed | Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet |
| title_short | Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet |
| title_sort | research on rolling bearing fault diagnosis method based on eca mranet |
| topic | fault diagnosis attention mechanism feature fusion ECA-MRANet |
| url | https://www.mdpi.com/2076-3417/14/2/551 |
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