Remaining Useful Life Prediction of Rolling Bearings Based on Cumulative KL Divergence and Improved Particle Filter
In view of the difficulty in predicting remaining useful life of bearing due to the neglection of cumulative degradation attribute of in-operation bearings in traditional degradation indexes and the particle degradation of conventional particle filter algorithm and insufficient particle diversity,a remaining useful life prediction method of rolling bearings was proposed based on feature cumulative KL divergence combined with improved particle filter.The cumulative KL divergence degradation index was constructed by converting the original KL divergence extracted from the bearing vibration signal into a mapping feature to optimize its monotonicity and tendency.A double exponential degradation model was established according to the degradation index,the sampling process of particle filter was optimized by using the gray wolf algorithm,and the residual resampling method is introduced to solve the particle degradation problem,so as to predict the remaining useful life with improved particle filter.Based on the 6312/C3 bearing run-to-failure experimental data and the XJTU-SY public bearing data set,the comparative experiment proves that the proposed cumulative KL divergence degradation index combined with the improved particle filter prediction method has higher prediction accuracy than the conventional one.
rolling bearingremaining useful lifecumulative KL divergenceparticle filter
万方数据
维普
