MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network base...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/71 |
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| author | Wenhan Huang Xiangfeng Zhang Hong Jiang Zhenfa Shao Yu Bai |
| author_facet | Wenhan Huang Xiangfeng Zhang Hong Jiang Zhenfa Shao Yu Bai |
| author_sort | Wenhan Huang |
| collection | DOAJ |
| description | In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method. |
| format | Article |
| id | doaj-art-ade26c5c0a314bb18b17cc0586af0716 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-ade26c5c0a314bb18b17cc0586af07162025-01-24T13:39:21ZengMDPI AGMachines2075-17022025-01-011317110.3390/machines13010071MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample ConditionsWenhan Huang0Xiangfeng Zhang1Hong Jiang2Zhenfa Shao3Yu Bai4School of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaIn complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method.https://www.mdpi.com/2075-1702/13/1/71fault diagnosisgenerative adversarial networksmall sampleattention mechanismminimum variance |
| spellingShingle | Wenhan Huang Xiangfeng Zhang Hong Jiang Zhenfa Shao Yu Bai MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions Machines fault diagnosis generative adversarial network small sample attention mechanism minimum variance |
| title | MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions |
| title_full | MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions |
| title_fullStr | MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions |
| title_full_unstemmed | MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions |
| title_short | MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions |
| title_sort | mcba mvacgan a novel fault diagnosis method for rotating machinery under small sample conditions |
| topic | fault diagnosis generative adversarial network small sample attention mechanism minimum variance |
| url | https://www.mdpi.com/2075-1702/13/1/71 |
| work_keys_str_mv | AT wenhanhuang mcbamvacgananovelfaultdiagnosismethodforrotatingmachineryundersmallsampleconditions AT xiangfengzhang mcbamvacgananovelfaultdiagnosismethodforrotatingmachineryundersmallsampleconditions AT hongjiang mcbamvacgananovelfaultdiagnosismethodforrotatingmachineryundersmallsampleconditions AT zhenfashao mcbamvacgananovelfaultdiagnosismethodforrotatingmachineryundersmallsampleconditions AT yubai mcbamvacgananovelfaultdiagnosismethodforrotatingmachineryundersmallsampleconditions |