A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

作     者：Chuang Chen Ningyun Lu Bin Jiang Cunsong Wang Chuang Chen;Ningyun Lu;Bin Jiang;Cunsong Wang

作者机构：College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China 

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

年 卷 期：2021年第8卷第2期

页      面：412-422页

核心收录：

学科分类：12[管理学] 0202[经济学-应用经济学] 02[经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 020205[经济学-产业经济学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：support by Natural Science Foundation of China(61873122) 

主　　题：Long short-term memory(LSTM)network predictive maintenance remaining useful life(RUL)estimation risk-averse adaptation support vector regression(SVR) 

摘      要：Remaining useful life(RUL)prediction is an advanced technique for system maintenance *** of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of *** this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation *** proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation ***,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,*** enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection *** designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization *** addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance *** is done using an aero-engine data set from *** results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.