Trajectory Enhanced Particle Filter and Its Application in Bearing Remaining Useful Life Prediction
The particle filter(PF)method is widely used in prediction of the remaining useful life(RUL)of rolling bearings.However,constructing a scientific prediction model and estimate appropriate model parameters is still a bottleneck.This limitation greatly affects the computational efficiency and stability of the PF method.To solve this problem,an RUL prediction method for rolling bearings is proposed based on trajectory enhanced particle filter(TE-PF).From the perspective of degradation rate tracking and degradation trajectory strengthen-ing,a universal prediction model based on the PF method is constructed,and the degradation trend information of historical samples and particle-generated samples are effectively used,which effectively guide the parameter estimation of the universal prediction model.Finally,a multi-information fusion trajectory enhancement predic-tion model is obtained.Experimental verification results show that the TE-PF method has stronger computa-tional efficiency and trend prediction stability than the traditional methods.In the case of observation sample ac-cumulation,the prediction accuracy in the confidence interval is higher.
rolling bearingsremaining useful life predictiondegradation rate trackingtrajectory enhanced particle filter
NETL
NSTL
万方数据
