Rolling bearing degradation trend prediction based on composite multiscale grey entropy and dynamic particle filter

Abstract

Abstract High accuracy prediction of degradation trend provides valuable information in establishing reasonable maintenance decision-making with the goal of improving the maintenance efficiency and avoiding sudden downtime. The extraction of degradation features and the prediction algorithm are the key factors in degradation trend prediction. In this work, based on composite multiscale grey entropy (CMGE) and dynamic particle filter (PF), a novel prediction architecture is proposed to improve accuracy under different working conditions. The CMGE is proposed as the degradation feature indicator extracted from rolling bearing vibration signal. The dynamic PF is proposed to predict the degradation trend of rolling bearing. Three rolling bearing accelerated life tests were conducted to evaluate the performance of the proposed method for rolling bearing degradation trend prediction. Experimental results demonstrate CMGE has good monotonicity and weak data length dependence, which can effectively describe the degradation trend of rolling bearing, and the proposed dynamic PF achieves higher prediction accuracy than the traditional PF and GM model, respectively.