Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

作     者：WANG Fengfei TANG Shengjin SUN Xiaoyan LI Liang YU Chuanqiang SI Xiaosheng WANG Fengfei;TANG Shengjin;SUN Xiaoyan;LI Liang;YU Chuanqiang;SI Xiaosheng

作者机构：Department of Mechanical EngineeringRocket Force University of EngineeringXi’an 710025China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2023年第34卷第1期

页      面：247-258页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China (61703410 61873175 62073336 61873273 61773386 61922089) 

主　　题：remaining useful life(RUL)prediction imperfect prior information failure time data nonlinear random coefficient regression(RCR)model 

摘      要：Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time ***, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.