Denoising Method for Bearing Vibration Signal Based on EEMD and Wavelet Packet Transform

Abstract

Aiming at the denoising problem of nonstationary and non-linear signals such as rolling bearing vibration signals, a signal denoising method is proposed using wavelet packet transform (WPT) combined with ensemble empirical mode decomposition (EEMD) to denoise the fault signals of rolling bearing. Firstly, the EEMD method is applied to decompose the vibration signals, and the intrinsic mode functions (IMFs) are sorted and classified according to the frequency values. Then, the correlation analysis of the high frequency IMFs including noise is implemented, and the higher correlation IMFs are further processed by the wavelet packet denoising approach. Finally, the denoised IMFs are reconstructed with other IMFs to achieve signal denoising. In order to verify the effectiveness of the proposed approach, the signal simulation analysis is implemented at first. The results show that the proposed method has a better denoising effect, which can effectively depress the noise of high-frequency signal compared with the traditional EMD and wavelet threshold denoising combination method, and ensure the integrity of the original signal to the greatest extent. Finally, the way is further applied to the denoising of bearing vibration signals, which further verifies the effectiveness of the proposed method.