Transfer Learning Based Data Feature Transfer for Fault Diagnosis
The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9076175/ |
| _version_ | 1818920785998249984 |
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| author | Wei Xu Yi Wan Tian-Yu Zuo Xin-Mei Sha |
| author_facet | Wei Xu Yi Wan Tian-Yu Zuo Xin-Mei Sha |
| author_sort | Wei Xu |
| collection | DOAJ |
| description | The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis often faces the problem of data scarcity. To overcome the lack of fault data, the transfer learning based on different working condition is gradually introduced into fault diagnosis by scholars. This paper discusses the current mainstream AI-based fault diagnosis methods, and analyzes the advantage of transfer learning for fault diagnosis problem. Then, a transfer component analysis (TCA) based method is proposed to transfer data features between different working conditions. Through the TCA-based method, the fault diagnosis model under the working condition can be established with the help of historical working condition. It effectively alleviates the problem of data scarcity under the condition to be predicted. Different from other fault diagnosis studies, this paper considers the online maintenance process based on TCA. A fault diagnosis framework including online maintenance process is proposed. Finally, a case study of bearing diagnosis from Case Western Reserve University proves the feasibility and effectiveness of the proposed TCA-based method and our fault diagnosis framework. |
| first_indexed | 2024-12-20T01:27:17Z |
| format | Article |
| id | doaj.art-65dba02ed1b846e394f653aec6f30dda |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:27:17Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-65dba02ed1b846e394f653aec6f30dda2022-12-21T19:58:12ZengIEEEIEEE Access2169-35362020-01-018761207612910.1109/ACCESS.2020.29895109076175Transfer Learning Based Data Feature Transfer for Fault DiagnosisWei Xu0https://orcid.org/0000-0003-2429-2258Yi Wan1https://orcid.org/0000-0002-4786-7125Tian-Yu Zuo2https://orcid.org/0000-0002-7049-6430Xin-Mei Sha3https://orcid.org/0000-0002-0691-0545School of Mechanical and Electrical Engineering, SanJiang University, Nanjing, ChinaSchool of Environmental Science, Nanjing Xiaozhuang University, Nanjing, ChinaSchool of Mechanical and Electrical Engineering, SanJiang University, Nanjing, ChinaSchool of Mechanical and Electrical Engineering, SanJiang University, Nanjing, ChinaThe development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis often faces the problem of data scarcity. To overcome the lack of fault data, the transfer learning based on different working condition is gradually introduced into fault diagnosis by scholars. This paper discusses the current mainstream AI-based fault diagnosis methods, and analyzes the advantage of transfer learning for fault diagnosis problem. Then, a transfer component analysis (TCA) based method is proposed to transfer data features between different working conditions. Through the TCA-based method, the fault diagnosis model under the working condition can be established with the help of historical working condition. It effectively alleviates the problem of data scarcity under the condition to be predicted. Different from other fault diagnosis studies, this paper considers the online maintenance process based on TCA. A fault diagnosis framework including online maintenance process is proposed. Finally, a case study of bearing diagnosis from Case Western Reserve University proves the feasibility and effectiveness of the proposed TCA-based method and our fault diagnosis framework.https://ieeexplore.ieee.org/document/9076175/Fault diagnosisfeature extractionfeature transfersensors |
| spellingShingle | Wei Xu Yi Wan Tian-Yu Zuo Xin-Mei Sha Transfer Learning Based Data Feature Transfer for Fault Diagnosis IEEE Access Fault diagnosis feature extraction feature transfer sensors |
| title | Transfer Learning Based Data Feature Transfer for Fault Diagnosis |
| title_full | Transfer Learning Based Data Feature Transfer for Fault Diagnosis |
| title_fullStr | Transfer Learning Based Data Feature Transfer for Fault Diagnosis |
| title_full_unstemmed | Transfer Learning Based Data Feature Transfer for Fault Diagnosis |
| title_short | Transfer Learning Based Data Feature Transfer for Fault Diagnosis |
| title_sort | transfer learning based data feature transfer for fault diagnosis |
| topic | Fault diagnosis feature extraction feature transfer sensors |
| url | https://ieeexplore.ieee.org/document/9076175/ |
| work_keys_str_mv | AT weixu transferlearningbaseddatafeaturetransferforfaultdiagnosis AT yiwan transferlearningbaseddatafeaturetransferforfaultdiagnosis AT tianyuzuo transferlearningbaseddatafeaturetransferforfaultdiagnosis AT xinmeisha transferlearningbaseddatafeaturetransferforfaultdiagnosis |