Fault Diagnosis of Low Speed Bearing Based on Acoustic Emission Signal and Multi-class Relevance Vector Machine

This study presents fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired from the low speed bearing test rig using two acoustic emission (AE) sensors under constant loading (5 kN) with different speed (20 rpm and 80 rpm). The aim of this study is addressed to search the reliable method for low speed machine fault diagnosis. In this paper, two methods of multi-class fault diagnosis based on classification techniques using RVM and SVM are presented. In present study, component analysis was performed to extract the feature and to reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that multi-class RVM gives promising technique for fault diagnosis of low speed machine.