Study on Fault Diagnosis of Rolling Bearing Based on K-L Transformation and Lagrange Support Vector Regression

Abstract

On the basis of vibration signal of rolling bearing, anew method of fault diagnosis based on K-L transformation and Lagrange support vector regression is presented.Multidimensional correlated variable is transformed into low dimensional independent eigenvector by the means of K-L Transformation. The pattern recognition and nonlinear regression are achieved by the method of Lagrange support vector regression. Lagrange support vector regression can be used to recognize the fault after be trained by the example data. Theory and experiment shows that the recognition of fault diagnosis of rolling bearing based on K-L transformation and Lagrange support vector regression theory is available to recognize the fault pattern accurately and provides a new approach to intelligent fault diagnosis.