Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities

Authors

  • Ruqiang Yan School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China https://orcid.org/0000-0002-1250-4084
  • Weihua Li School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, China
  • Siliang Lu College of Electrical Engineering and Automation, Anhui University, Hefei, China
  • Min Xia Department of Mechanical & Materials Engineering, Western University, Ontario, Canada https://orcid.org/0000-0001-8057-9654
  • Zhuyun Chen School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, China & State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China
  • Zheng Zhou School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
  • Yasong Li School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
  • Jingfeng Lu College of Electrical Engineering and Automation, Anhui University, Hefei, China

DOI:

https://doi.org/10.37965/jdmd.2024.530

Abstract

As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management (PHM) domain, transfer learning provides a fundamental solution to enhance generalization of data-driven methods. In this paper, we briefly discuss general idea and advances of various transfer learning techniques for PHM domain, including domain adaptation, domain generalization, federated learning, and knowledge driven transfer learning. Based on the observations from state of the art, we provide extensive discussions on possible challenges and opportunities of transfer learning for PHM domain to direct future development.

 

Conflict of Interest Statement

The authors declare no conflicts of interest.

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Published

2024-05-14

How to Cite

Yan, R., Li, W., Lu, S., Xia, M., Chen, Z., Zhou, Z., Li, Y., & Lu, J. (2024). Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities. Journal of Dynamics, Monitoring and Diagnostics. https://doi.org/10.37965/jdmd.2024.530

Issue

Section

Future Direction Paper