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

作　　者：JDMD Editorial Office Nagi Gebraeel Yaguo Lei Naipeng Li Xiaosheng Si Enrico Zio 

机构地区：[1]Editorial Office of JDMD,Chongqing University of Technology,Chongqing,People’s Republic of China [2]H.Milton Stewart School of Industrial and Systems Engineering,Georgia Institute of Technology,Atlanta,GA,USA [3]Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi’an Jiaotong University,Xi’an,People’s Republic of China [4]Zhijian Laboratory,Rocket Force University of Engineering,Xi’an,People’s Republic of China [5]MINES Paris,PSL Research University,Sophia Antipolis,France [6]Energy Department,Politecnico di Milano,Milan,Italy

出　　处：《Journal of Dynamics, Monitoring and Diagnostics》2023年第1期1-12,共12页动力学、监测与诊断学报（英文）

基　　金：The work in Section III was supported by the National Science Foundation of China(NSFC)(Nos.52025056,52005387);the work in Section IV was supported by the National Science Foundation of China(NSFC)(Nos.62233017,62073336).

摘　　要：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 fromthe 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.

关 键 词：PROGNOSTICS remaining useful life DATA-DRIVEN machine learning degradation modeling 

分 类 号：TH12[机械工程—机械设计及理论] TP3[自动化与计算机技术—计算机科学与技术]