In the diagnosis of bearing

the LS-SVM method research with LMD morphological filtering was put out in order to solve the problem about the kernel function parameter selection and the bad sparsity of least squares vector machine (LS-SVM).First

the LMD was used to decompose the measured signal and PF components were obtained.The correlation analysis was carried out to remove the false components

and the noise of PF components was reduced by morphological filtering.The LMD decomposed the recombinational signal and obtained new PF components

and energy characteristics were got from the new PF component.Secondly

the kernel function of LS-SVM is improved to solve the problem of kernel parameter selection. Lagrange parameters were weighted by feature weighting method

and their weighted average value was taken as the threshold of pruning method to reduce the sparsity. Finally

energy characteristics were put into LS-SVM to train and predict.Experiments showed that this new method could fulfil adaptive classification of bearing fault signals and definite fault conclusion quickly and effectively.