Fault Pattern Recognition of Bearing Based on Principal Components Analysis and Support Vector Machine

State monitoring and fault diagnosing of rolling bearing by analyzing vibrating signal is one of the major problem which need to be solved in mechanical engineering. In this paper, a new method of fault diagnosis based on principal components analysis and support vector machine is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of rolling bearing. The key to the fault bearings diagnosis is feature extracting and feature classifying. Multidimensional correlated variable is converted into low dimensional independent eigenvector by means of principal components analysis. The pattern recognition and the nonlinear regression are achieved by the method of support vector machine. In the light of the feature of vibrating signals, eigenvector is obtained using singularity value decomposition, fault diagnosis of rolling bearing is recognized correspondingly using support vector machine multiple fault classifier. Theory and experiment show that the recognition of fault diagnosis of rolling bearing based on principal components analysis and support vector machine theory is available in the fault pattern recognizing and provides a new approach to intelligent fault diagnosis.