19th Asia-Pacific Vibration Conference (APVC2021)

Rolling bearings are the most widely used transmissions in mechanical equipment. However, they are prone to failure due to their complex structure and harsh working environment. Therefore, monitoring the rolling bearing’s working state is of great significance. This paper proposes a fault diagnosis method for rolling bearings based on the wavelet threshold denoising and Fast spectral correlation (Fast-SC). Firstly, the wden function is used to perform 5-layer wavelet decomposition on the original signal, and then the inverse transform of the wavelet coefficients after threshold processing is applied to reconstruct the denoised signal. Finally, the denoised signal is analyzed by Fast-SC to identify the rolling bearing fault features. The results show that the proposed method can be effectively applied to simulation analysis and experimental data. By comparing with Fast-SC and envelope spectrum, it proves that this method is an effective method for extracting fault features of rolling bearings.

Fast spectral correlation; Wavelet denoising; Rolling element bearing; Feature extraction