An Intelligent Fault Diagnosis Method Based on Multiscale Entropy and SVMs
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
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In this paper, a new method of fault diagnosis based on K-L transform and support vector machine(SVM) is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of ball 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 K-L transform. 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 K-L transform, fault diagnosis of ball bearing is recognized correspondingly using support vector machine multiple fault classifier. Theory and experiment show that the recognition of fault diagnosis of ball bearing based on K-L transform and support vector machine theory is available in the fault pattern recognizing and provides a new approach to the development of intelligent fault diagnosis.