A nonlinear degradation model based method for remaining useful life prediction of rolling element bearings

This paper proposes a nonlinear degradation model based method for remaining useful life (RUL) prediction of rolling element bearings. First, a new nonlinear degradation model is constructed which considers four variable sources of stochastic degradation processes of bearings simultaneously, i.e., the temporal variability, the unit-to-unit variability, the measurement variability and the nonlinear variability. Then a Kalman particle filtering (KPF) algorithm is applied to estimate the state and predict the RUL of bearings. The effectiveness of the nonlinear degradation model based method is demonstrated using simulated degradation processes and accelerated degradation tests of rolling element bearings. The results show that the nonlinear model performs better than the linear model in describing the degradation processes of bearings, and KPF is more effective in the state estimation and RUL prediction of bearings than the Kalman filtering and the particle filtering algorithms.