Degradation trend prediction of rolling bearing vibration performance based on fusion grey entropy and bootstrap Markov chain

In view of the shortcomings of existing entropy-based nonlinear dynamics methods that the calculation results are inconsistent with the nonlinear dynamics system at different scales and the data length required for the calculation is long，the fusion gray entropy algorithm，a new measure of nonlinear time series complexity，was proposed and then used to extract the degradation features of rolling bearing. Considering the problems of very short data length of the rolling bearing degradation trend sequence that it is difficult for prediction， Bootstrap Markov chain prediction model was proposed. The experimental results showed that the data length requirement of fusion gray entropy was low，and the calculation results of the fusion gray entropy at different scales were consistent. Meanwhile，the average relative error of the proposed bootstap Markov chain prediction model was only 8.497 3%，which was lower than that of the GM model. This showed that the proposed model can effectively predict the vibration performance degradation trend of rolling bearings. © 2023 BUAA Press. All rights reserved.