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.
Highlights
Remaining useful life (RUL) prediction of the equipment is the key technology for realizing condition based maintenance (CBM)
This paper presents a novel equipment RUL prediction method based on the particle filter (PF) algorithm and genetic algorithm (GA)
Model is constructed by n symptom parameters obtained from time ts to tp ; Step 4: Use the Genetic algorithm (GA)-PF to estimate symptom parameter values of the time period {tp + 1, tp + 2 . . . } until the estimated symptom parameter is larger than the preset breakdown threshold; Step 5: RUL value is obtained with Equation (26); Step 6: Calculating the (n + 1)th symptom parameter at time tp + 1, and time-varying auto regressive (TVAR) model is updated with the new symptom parameter; Step 7: The new estimation is conducted by using the GA-PF and the updated TVAR model until the estimated symptom parameter is larger than the preset breakdown threshold, and the new RUL
Summary
Remaining useful life (RUL) prediction of the equipment is the key technology for realizing condition based maintenance (CBM). Many works about equipment RUL prediction have been reported These works can be categorized into model-based and data-driven methods. In [5], a bearing RUL was predicted by using Paris crack growth model and GA algorithm. The model-based methods are hardly applied to predict the RUL of machinery in real time. In [8], Chen et al proposed a reliability estimation method based on logistic regression model, and applied it to predict the RUL of cutting tool. Among all the data-driven methods, the particle filter (PF) technique is one of the most effective for nonlinear and non-Gaussian systems, which can accurately estimate the future state of mechanical systems. This paper presents a novel equipment RUL prediction method based on the PF algorithm and genetic algorithm (GA).
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