RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

作     者：Qiwu Zhu Qingyu Xiong Zhengyi Yang Yang Yu Qiwu Zhu;Qingyu Xiong;Zhengyi Yang;Yang Yu

作者机构：School of Big Data&Software EngineeringChongqing UniversityChongqing 400044 Key Laboratory of Dependable Service Computing in Cyber Physical SocietyMinistry of EducationChongqing 400044China School of Biology and EngineeringGuizhou Medical UniversityGuiyang 550025China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2023年第10卷第7期

页      面：1640-1642页

核心收录：

学科分类：07[理学] 08[工学] 081104[工学-模式识别与智能系统] 0837[工学-安全科学与工程] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Major Special Program of Chongqing Science&Technology Commission(CSTC 2019jscx-zdztzx X0031) Graduate Scientific Research and Innovation Foundation of Chongqing(CYB21068,CYS22128)。 

主　　题：letter estimation prediction 

摘      要：Dear Editor,This letter focuses on the problem of remaining useful life(RUL)prediction of equipment. Existing graph neural network(GCN)-based approaches merely provide the point estimation of RUL. However,the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision making.To map the relationship between noisy condition monitoring data and RUL with uncertainty.