Research on early warning of rolling bearing wear failure based on empirical mode decomposition

Abstract

In order to solve the problems of low precision and long time-consuming of traditional methods, this paper designs a rolling bearing wear fault early warning method based on empirical mode decomposition (EMD). Based on the wear reason of rolling bearing, the acceleration sensor is used to collect its vibration signal, and the EMD algorithm is used to stabilise the signal to obtain multi-scale signal. Each multi-scale signal is decomposed into sub-band to get multi-scale sub-band signal, then the multi-scale sub-band sample entropy is obtained, and the optimisation function of local preserving projection algorithm is constructed to obtain the eigenvalue and eigenvector of wear failure fault, and finally the fault early warning is realised. The simulation results show that the signal denoising effect of this method is good, the early warning accuracy is always above 94%, and the average alarm time is close to 0.27 s.