Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges

Authors

  • JDMD Editorial Office Chongqing University of Technology, Chongqing, China
  • Nagi Gebraeel H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA https://orcid.org/0000-0001-7337-2401
  • Yaguo Lei Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, P. R. China https://orcid.org/0000-0002-5167-1459
  • Naipeng Li Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, P. R. China
  • Xiaosheng Si Zhijian Laboratory, Rocket Force University of Engineering, Xi'an 710025, P. R. China
  • Enrico Zio MINES Paris, PSL Research University, Sophia Antipolis, France & Energy Department, Politecnico di Milano, Milan, Italy https://orcid.org/0000-0002-7108-637X

DOI:

https://doi.org/10.37965/jdmd.2023.148

Keywords:

Prognostics, remaining useful life, data-driven, machine learning, degradation modeling.

Abstract

As the fundamental and key technique to ensure the safe and reliable operation of vital systems, prognostics with an emphasis on the remaining useful life (RUL) prediction has attracted great attention in the last decades. In this paper, we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery, mainly including data-driven methods, physics-based methods, hybrid methods, etc. Based on the observations from the state of the art, we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

 

Conflict of Interest Statement

The authors declare no conflicts of interest.

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Published

2023-02-20

How to Cite

JDMD Editorial Office, Gebraeel, N., Lei, Y., Li, N., Si, X., & Zio, E. (2023). Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges. Journal of Dynamics, Monitoring and Diagnostics, 2(1), 1–12. https://doi.org/10.37965/jdmd.2023.148

Issue

Section

Future Direction Paper