There Search of LS-SVM Based on LMD Morphology Filter - MENG Liang; XU Tong-le; MA Jin-ying; CAI Dao-yong (Journal of Harbin University of Science and Technology)
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.