Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Abstract

Aiming at the difficulty of mining fault prognosis starting points and constructing prognostic models for remaining useful life (RUL) prediction of rolling bearings, a RUL prediction method is proposed based on health indicator (HI) extraction and trajectory-enhanced particle filter (TE-PF). By extracting a HI that can accurately track the trending of bearing degradation and combining it with the early fault enhancement technology, early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction models. Aiming at the problem that traditional degradation rate models based on PF are vulnerable to HI mutations, a TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to timely modify prediction model parameters. Results from a rolling bearing prognostic study show that prediction starting points can be accurately detected and a reasonable prediction model can be conveniently constructed by the RUL prediction method based on HI amplitude abnormal detection and TE-PF. Furthermore, aiming at the RUL prediction problem under the condition of HI mutation, RUL prediction with probability and statistics characteristics under a confidence interval can be obtained based on the method proposed.