Rolling bearing fault diagnosis method based on ELMD hybrid feature extraction and wavelet neural network

Abstract

Rolling bearings are the most important components in the transmission system of coal mining machinery, and their operating condition significantly impacts the entire mechanical and electrical equipment. Therefore, the fault diagnosis of rolling bearing can effectively ensure the operation reliability of equipment. Given the strong noise, coal impact, and other interference, the vibration signal of the rolling bearing cannot be effectively decomposed, and the fault identification efficiency is low. According to the method based on vibration analysis, this article proposes a rolling bearing fault diagnosis method based on ensemble local mean decomposition (ELMD) hybrid feature extraction and wavelet neural network. ELMD is used to solve the problem of modal aliasing in local mean decomposition (LMD), which can improve the efficiency of LMD. Quantitatively extracting the mixed features of each component and introducing a wavelet neural network for fault type recognition. The experimental results demonstrate that the proposed method has a high accuracy in fault recognition and is an effective fault diagnosis method.