A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is a key process in condition-based maintenance for machines. It contributes to reducing risks and maintenance costs and increasing the maintainability, availability, reliability, and productivity of machines. This paper proposes a new method based on stochastic process models for machine RUL prediction. First, a new stochastic process model is constructed considering the multiple variability sources of machine stochastic degradation processes simultaneously. Then the Kalman particle filtering algorithm is used to estimate the system states and predict the RUL. The effectiveness of the method is demonstrated using simulated degradation processes and accelerated degradation tests of rolling element bearings. Through comparisons with other methods, the proposed method presents its superiority in describing the stochastic degradation processes and predicting the machine RUL.