A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning
Current studies on intelligent bearing fault diagnosis based on transfer learning have been fruitful. However, these methods mainly focus on transfer fault diagnosis of bearings under different working conditions. In engineering practice, it is often difficult or even impossible to obtain a large am...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5540084 |
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| author | Shoubing Xiang Jiangquan Zhang Hongli Gao Dalei Shi Liang Chen |
| author_facet | Shoubing Xiang Jiangquan Zhang Hongli Gao Dalei Shi Liang Chen |
| author_sort | Shoubing Xiang |
| collection | DOAJ |
| description | Current studies on intelligent bearing fault diagnosis based on transfer learning have been fruitful. However, these methods mainly focus on transfer fault diagnosis of bearings under different working conditions. In engineering practice, it is often difficult or even impossible to obtain a large amount of labeled data from some machines, and an intelligent diagnostic method trained by labeled data from one machine may not be able to classify unlabeled data from other machines, strongly hindering the application of these intelligent diagnostic methods in certain industries. In this study, a deep transfer learning method for bearing fault diagnosis, domain separation reconstruction adversarial networks (DSRAN), was proposed for the transfer fault diagnosis between machines. In DSRAN, domain-difference and domain-invariant feature extractors are used to extract and separate domain-difference and domain-invariant features, respectively Moreover, the idea of generative adversarial networks (GAN) was used to improve the network in learning domain-invariant features. By using domain-invariant features, DSRAN can adopt the distribution of the data in the source and target domains. Six transfer fault diagnosis experiments were performed to verify the effectiveness of the proposed method, and the average accuracy reached 89.68%. The results showed that the DSRAN method trained by labeled data obtained from one machine can be used to identify the health state of the unlabeled data obtained from other machines. |
| format | Article |
| id | doaj-art-c764930a263f40899086be6e3a59cac5 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c764930a263f40899086be6e3a59cac52025-02-03T06:12:05ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/55400845540084A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial LearningShoubing Xiang0Jiangquan Zhang1Hongli Gao2Dalei Shi3Liang Chen4School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaCurrent studies on intelligent bearing fault diagnosis based on transfer learning have been fruitful. However, these methods mainly focus on transfer fault diagnosis of bearings under different working conditions. In engineering practice, it is often difficult or even impossible to obtain a large amount of labeled data from some machines, and an intelligent diagnostic method trained by labeled data from one machine may not be able to classify unlabeled data from other machines, strongly hindering the application of these intelligent diagnostic methods in certain industries. In this study, a deep transfer learning method for bearing fault diagnosis, domain separation reconstruction adversarial networks (DSRAN), was proposed for the transfer fault diagnosis between machines. In DSRAN, domain-difference and domain-invariant feature extractors are used to extract and separate domain-difference and domain-invariant features, respectively Moreover, the idea of generative adversarial networks (GAN) was used to improve the network in learning domain-invariant features. By using domain-invariant features, DSRAN can adopt the distribution of the data in the source and target domains. Six transfer fault diagnosis experiments were performed to verify the effectiveness of the proposed method, and the average accuracy reached 89.68%. The results showed that the DSRAN method trained by labeled data obtained from one machine can be used to identify the health state of the unlabeled data obtained from other machines.http://dx.doi.org/10.1155/2021/5540084 |
| spellingShingle | Shoubing Xiang Jiangquan Zhang Hongli Gao Dalei Shi Liang Chen A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning Shock and Vibration |
| title | A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning |
| title_full | A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning |
| title_fullStr | A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning |
| title_full_unstemmed | A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning |
| title_short | A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning |
| title_sort | deep transfer learning method for bearing fault diagnosis based on domain separation and adversarial learning |
| url | http://dx.doi.org/10.1155/2021/5540084 |
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