A fault pulse extraction and feature enhancement method for bearing fault diagnosis

Abstract Generally, the transient characteristics of early bearing failure are not obvious. How to extract weak transient features is a big challenge. Dictionary learning has been successfully used to extract bearing fault features. However, the traditional dictionary learning is easy to fall into local optimum and cannot extract fault features from complex signals. And it often consumes huge computational costs. In order to solve the above problems, this paper proposes a fault pulse extraction and feature enhancement method for bearing fault diagnosis. Firstly, the bearing vibration signal is segmented in the time domain. Then this paper proposes a multi-scale alternating direction multiplier method for dictionary learning (MADMMDL) to extract fault impact signal from the segment signal. Finally, frequency spectrum averaging is used to enhance the bearing fault characteristic frequency. Through numerical simulation and rail transit transmission failure simulation experimental analysis, the feasibility of this method in bearing fault diagnosis is verified.