Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing
Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure m...
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| Veröffentlicht in: | Reliability engineering & system safety Jg. 234; S. 109186 |
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| Hauptverfasser: | , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.06.2023
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| Schlagworte: | |
| ISSN: | 0951-8320, 1879-0836 |
| Online-Zugang: | Volltext |
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| Abstract | Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.
•A digital twin-driven intelligent diagnosis method is developed.•A high-fidelity digital twin model is built for the rolling bearing.•A partial domain adaptation algorithm is introduced for bearing condition assessment.•One test is conducted to validate the effectiveness of the proposed methodology. |
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| AbstractList | Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.
•A digital twin-driven intelligent diagnosis method is developed.•A high-fidelity digital twin model is built for the rolling bearing.•A partial domain adaptation algorithm is introduced for bearing condition assessment.•One test is conducted to validate the effectiveness of the proposed methodology. |
| ArticleNumber | 109186 |
| Author | Gu, Fengshou Zhang, Yongchao Ni, Qing Lei, Zihao Yu, Kun Feng, Ke Ge, Jian Ji, J.C. Ren, Zhaohui Liu, Zheng |
| Author_xml | – sequence: 1 givenname: Yongchao orcidid: 0000-0001-5892-3391 surname: Zhang fullname: Zhang, Yongchao organization: School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 2 givenname: J.C. surname: Ji fullname: Ji, J.C. organization: School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia – sequence: 3 givenname: Zhaohui surname: Ren fullname: Ren, Zhaohui email: renzhh_neu@126.com organization: School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 4 givenname: Qing surname: Ni fullname: Ni, Qing organization: School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia – sequence: 5 givenname: Fengshou surname: Gu fullname: Gu, Fengshou organization: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK – sequence: 6 givenname: Ke surname: Feng fullname: Feng, Ke email: ke.feng@outlook.com organization: School of Engineering, University of British Columbia, Kelowna, British Columbia V1V 1V7, Canada – sequence: 7 givenname: Kun surname: Yu fullname: Yu, Kun organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China – sequence: 8 givenname: Jian surname: Ge fullname: Ge, Jian organization: School of Engineering, University of British Columbia, Kelowna, British Columbia V1V 1V7, Canada – sequence: 9 givenname: Zihao surname: Lei fullname: Lei, Zihao organization: School of Engineering, University of British Columbia, Kelowna, British Columbia V1V 1V7, Canada – sequence: 10 givenname: Zheng surname: Liu fullname: Liu, Zheng organization: School of Engineering, University of British Columbia, Kelowna, British Columbia V1V 1V7, Canada |
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