A problem of mode mixing occurred in implementation process of local mean decomposition (LMD) method

a fault diagnosis approach for rolling bearing based on ensemble local mean decomposition (ELMD) and least squares support vector machine (LS-SVM) was proposed.Firstly

by using ELMD method

the vibrational signal of rolling bearing was decomposed a series of product function (PF) components

and then the PF components which contain main fault information were selected

and the kurtosis coefficients and energy characteristic parameters extracted from selected PF components were regarded as fault feature which was served as input parameters of LS-SVM to identify the working status and fault types of rolling bearing.The analytic results of fault-free

inner-race fault and outer-race fault of rolling bearing indicate that the working status and fault types of rolling bearing can be identified accurately and effectively by using the approach based on ELMD and LS-SVM.