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Sensors 2017, 17(4), 696; doi:10.3390/s17040696

New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction

1
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China
2
Graduate School of Bioresources, Mie University/1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 5 January 2017 / Revised: 16 March 2017 / Accepted: 20 March 2017 / Published: 28 March 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3716 KB, uploaded 28 March 2017]   |  

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. View Full-Text
Keywords: remaining useful life; particle filter; genetic algorithm; starting prediction time; time-varying auto regressive remaining useful life; particle filter; genetic algorithm; starting prediction time; time-varying auto regressive
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Li, K.; Wu, J.; Zhang, Q.; Su, L.; Chen, P. New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction. Sensors 2017, 17, 696.

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