Bearing fault diagnosis based on Alpha-stable distribution feature extraction and SVM classifier

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by Alpha-stable distribution parameters, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance.