The Application of PCA and SVM in Rolling Bearing Fault Diagnosis

Abstract

The key to the fault diagnosis is feature extracting and fault pattern classifying. Principal components analysis (PCA) and support vector machine (SVM) method are introduced to recognize the fault pattern of the rolling bearing in this paper. Multidimensional correlated variable is converted into low dimensional independent eigenvector by means of PCA. The pattern recognition and the nonlinear regression are achieved by the method of SVM. In the light of the feature of vibrating signals, eigenvector is obtained using PCA, fault diagnosis of rolling bearing is recognized correspondingly using SVM fault classifier. Theory and experiment show that the recognition of fault diagnosis of rolling bearing based on PCA and SVM theory is available in the fault pattern recognition and provides a new approach to intelligent fault diagnosis.