Fault classification of rolling bearing based on LMD-sample entropy and LS-SVM

In view of the nonlinear and non-stationary characteristics of vibration signals for rolling bearings, a fault classification method of rolling bearing based on local mean decomposition (LMD)-sample entropy and Least Squares Support Vector Machines (LS-SVM) was proposed. LMD method was employed to decompose vibration signals of rolling bearings into several product function components, and sample entropy of the first few PF components containing main fault information was selected as the characteristic vectors. Then, LS-SVM was used to analyze and identify vibration signals of normal bearing, inner race faulty bearing and outer race faulty bearing. The results show that the method proposed in the paper can classify various states of rolling bearings effectively.