A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis
In actual industry applications, the failure categories of practical equipment are usually a subset of laboratory conditions failure categories. Due to the strict constraints, partial transfer learning can address a more practical diagnostic scenario. In view of this, this paper proposes a target do...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Yin Bai Xiangdong Hu Kai Zheng Yunnong Chen Yi Tang |
| author_facet | Yin Bai Xiangdong Hu Kai Zheng Yunnong Chen Yi Tang |
| author_sort | Yin Bai |
| collection | DOAJ |
| description | In actual industry applications, the failure categories of practical equipment are usually a subset of laboratory conditions failure categories. Due to the strict constraints, partial transfer learning can address a more practical diagnostic scenario. In view of this, this paper proposes a target domain-specific classifier weight partial transfer adversarial network. Initially, the 1-D convolutional neural network is employed as the basic architecture. By training the domain discriminator and feature generator with an adversarial strategy, the recognition ability of the domain discriminant network and the feature extraction ability of the feature generation network can be enhanced. After that, a weighted learning strategy is introduced to guide the model to learn the cross-domain invariant feature. Also, a specific target domain classifier is utilized to redivide the target domain decision boundary to accurately classify the unlabeled target domain samples. Finally, five mainstream deep neural network methods are taken for comparison using the data from Western Reserve University and the motor-magnetic brake test designed by us. The results show that the proposed method reaches 90.18% and 96.53% classification accuracy on two datasets, respectively, which demonstrates superior performance compared with the state-of-the-art methods. |
| format | Article |
| id | doaj-art-e4d8de76ddad4fc997514f23a09b1fbd |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e4d8de76ddad4fc997514f23a09b1fbd2025-01-24T13:39:53ZengMDPI AGMathematics2227-73902025-01-0113224810.3390/math13020248A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault DiagnosisYin Bai0Xiangdong Hu1Kai Zheng2Yunnong Chen3Yi Tang4School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Modern Posts, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310058, ChinaSchool of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaIn actual industry applications, the failure categories of practical equipment are usually a subset of laboratory conditions failure categories. Due to the strict constraints, partial transfer learning can address a more practical diagnostic scenario. In view of this, this paper proposes a target domain-specific classifier weight partial transfer adversarial network. Initially, the 1-D convolutional neural network is employed as the basic architecture. By training the domain discriminator and feature generator with an adversarial strategy, the recognition ability of the domain discriminant network and the feature extraction ability of the feature generation network can be enhanced. After that, a weighted learning strategy is introduced to guide the model to learn the cross-domain invariant feature. Also, a specific target domain classifier is utilized to redivide the target domain decision boundary to accurately classify the unlabeled target domain samples. Finally, five mainstream deep neural network methods are taken for comparison using the data from Western Reserve University and the motor-magnetic brake test designed by us. The results show that the proposed method reaches 90.18% and 96.53% classification accuracy on two datasets, respectively, which demonstrates superior performance compared with the state-of-the-art methods.https://www.mdpi.com/2227-7390/13/2/248domain-specific classifierweight partial transfer adversarial networkbearing fault diagnosistransfer learning |
| spellingShingle | Yin Bai Xiangdong Hu Kai Zheng Yunnong Chen Yi Tang A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis Mathematics domain-specific classifier weight partial transfer adversarial network bearing fault diagnosis transfer learning |
| title | A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis |
| title_full | A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis |
| title_fullStr | A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis |
| title_full_unstemmed | A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis |
| title_short | A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis |
| title_sort | target domain specific classifier weight partial transfer adversarial network for bearing fault diagnosis |
| topic | domain-specific classifier weight partial transfer adversarial network bearing fault diagnosis transfer learning |
| url | https://www.mdpi.com/2227-7390/13/2/248 |
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