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

JDMD Editorial Office; Nagi Gebraeel; Yaguo Lei; Naipeng Li; Xiaosheng Si; Enrico Zio

doi:10.37965/jdmd.2023.148

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.