Fault Diagnosis Research on Bearingof Motor Based on LMD And Support Vector Machine - YIN Zhao-jie; XU Tong-le; ZHENG Dian-kun (Journal of Harbin University of Science and Technology)
Aiming at the limitation of the support vector machine ( SVM) to deal with the large sample dataand the multi classification problem and the selection of kernel function，the fault diagnosis method of motor bearing based on LMD and support vector machine is proposed. Firstly，the local mean decomposition ( LMD) algorithm is used to adaptively decompose the signal to get a series of PF components ，and the correlation analysis is used to eliminate false components. Then，the energy feature vector is formed by extracting energy of the real PF component. Secondly，the new kernel function is used to improve the SVM to complete the adaptive training，and the“one to many”method is used to solve the large sample data and multi classification problem. Finally，the energy feature vector is used as the training sample and test sample of SVM，and the fault information of motor bearing is trained and predicted. Experimental results show that the proposed method can effectively diagnose the fault of motor bearing.