title: New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction.
abstract: Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.
authors.affiliation: Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China. dayanlv@live.cn.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China. wjjlady720@jiangnan.edu.cn.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Wu
authors.affiliation: Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China. qjzhang@jiangnan.edu.cn.
authors.forename: Qiuju
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China. lei_su2015@jiangnan.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Su
authors.affiliation: Graduate School of Bioresources, Mie University/1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan. chen@bio.mie-u.ac.jp.
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
keywords: genetic algorithm; particle filter; remaining useful life; starting prediction time; time-varying auto regressive