STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT)
The complex and changeable working conditions of mechanical equipment in industrial field lead to uneven distribution of fault samples, which brings great trouble to traditional machine learning. In order to solve this problem, proposes a bearing fault transfer diagnosis method based on domain adapt...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.03.001 |
| _version_ | 1797767615120146432 |
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| author | WANG TingXuan WANG GuiYong LIU Tao WANG ZhenYa |
| author_facet | WANG TingXuan WANG GuiYong LIU Tao WANG ZhenYa |
| author_sort | WANG TingXuan |
| collection | DOAJ |
| description | The complex and changeable working conditions of mechanical equipment in industrial field lead to uneven distribution of fault samples, which brings great trouble to traditional machine learning. In order to solve this problem, proposes a bearing fault transfer diagnosis method based on domain adaptive neural network and balanced dynamic distribution adaptive. Firstly, According to the characteristics of bearing vibration fault samples, the convolution layer of convolutional neural network is improved by wavelet transform, and the characteristics of bearing samples are extracted adaptively. Then, Maximum Mean Discrepancy measure and weight regularization are used to process the generated features in the loss function to reduce the difference in sample distribution and obtain the domain adaptive neural network model. Finally, A-distance is used to improve the equilibrium distribution adaptive to make it have dynamic characteristics, further improve the difference of sample distribution, and realize bearing transfer diagnosis by KNN classifier. Through experimental verification, the proposed method can accurately migrate the bearing fault state in the same bench rig cases and cross bench rig cases, proving that the method can effectively solve the problem of uneven distribution of unlabeled samples under variable working conditions, and has the effectiveness and robustness. |
| first_indexed | 2024-03-12T20:41:23Z |
| format | Article |
| id | doaj.art-9f5e9bc79d724b3488524f3908759d1f |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:41:23Z |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-9f5e9bc79d724b3488524f3908759d1f2023-08-01T07:55:39ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-0150951840367113STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT)WANG TingXuanWANG GuiYongLIU TaoWANG ZhenYaThe complex and changeable working conditions of mechanical equipment in industrial field lead to uneven distribution of fault samples, which brings great trouble to traditional machine learning. In order to solve this problem, proposes a bearing fault transfer diagnosis method based on domain adaptive neural network and balanced dynamic distribution adaptive. Firstly, According to the characteristics of bearing vibration fault samples, the convolution layer of convolutional neural network is improved by wavelet transform, and the characteristics of bearing samples are extracted adaptively. Then, Maximum Mean Discrepancy measure and weight regularization are used to process the generated features in the loss function to reduce the difference in sample distribution and obtain the domain adaptive neural network model. Finally, A-distance is used to improve the equilibrium distribution adaptive to make it have dynamic characteristics, further improve the difference of sample distribution, and realize bearing transfer diagnosis by KNN classifier. Through experimental verification, the proposed method can accurately migrate the bearing fault state in the same bench rig cases and cross bench rig cases, proving that the method can effectively solve the problem of uneven distribution of unlabeled samples under variable working conditions, and has the effectiveness and robustness.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.03.001Bearing;Domain adaptive neural networks;Balanced distribution adaption;Wavelet transform;A-distance;Transfer diagnosis |
| spellingShingle | WANG TingXuan WANG GuiYong LIU Tao WANG ZhenYa STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) Jixie qiangdu Bearing;Domain adaptive neural networks;Balanced distribution adaption;Wavelet transform;A-distance;Transfer diagnosis |
| title | STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) |
| title_full | STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) |
| title_fullStr | STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) |
| title_full_unstemmed | STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) |
| title_short | STUDY OF DOMAIN ADAPTIVE NETWORK AND BALANCED DYNAMIC DISTRIBUTION ADAPTIVE FAULT MIGRATION DIAGNOSIS OF BEARING UNDER VARIABLE CONDITIONS (MT) |
| title_sort | study of domain adaptive network and balanced dynamic distribution adaptive fault migration diagnosis of bearing under variable conditions mt |
| topic | Bearing;Domain adaptive neural networks;Balanced distribution adaption;Wavelet transform;A-distance;Transfer diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.03.001 |
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