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

Ruqiang Yan; Weihua Li; Siliang Lu; Min Xia; Zhuyun Chen; Zheng Zhou; Yasong Li; Jingfeng Lu

doi:10.37965/jdmd.2024.530

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

Keywords:

domain adaptation; domain generalization; federated learning; knowledge-driven; PHM; transfer learning

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.