Multi-Condition Rolling Bearing Fault Denoising Method and Application Based on RIME-VMD

Abstract

To improve the stability of rolling bearing fault signal denoising under different working conditions, this study proposes a multi-condition rolling bearing fault denoising method based on RIME-VMD. According to the characteristics of rolling bearing fault signal, RIME (frost and ice algorithm) is utilized to obtain adaptive optimization of the modal number and penalty factors in VMD (variational mode decomposition) algorithm, and then the optimized core parameters are input into VMD to decompose the rolling bearing fault signal. The fitness function is established by introducing the fusion of power spectrum entropy and kurtosis value; these intrinsic modal functions (IMFs) with high correlation based on original rolling bearing fault signal are selected to reconstruct the rolling bearing fault denoising signal. The experimental results show that the RIME-VMD method can effectively remove most of the noise in the rolling bearing fault signal, and the performance evaluation indexes of this method are better than other existing optimization algorithms. The research achievement of this study can provide effective data support for the fault diagnosis of bearing equipment.