Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges
Keywords:Prognostics, remaining useful life, data-driven, machine learning, degradation modeling.
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.