Affiliations: School of Mechanical Engineering, Shandong University of Technology, Zibo, China
Correspondence:
[*]
Corresponding author. Tongle Xu, School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China. Tel.: +8613964413228; Fax: +8605332786982; E-mail: xutongle@163.com.
Abstract: In order to improve the accuracy of fault diagnosis, a fault diagnosis method for rotating machinery based on Local Mean Decomposition (LMD) morphological filtering and Least Square Support Vector Machine(LS-SVM) is proposed. Firstly, the collected vibration signals are processed by LMD which decomposes them into a series of product function (PF) components, and PF components are carried out by method of the morphological filtering and signal recombination. Then, the feature vector is obtained from the new PF components by LMD method. Secondly, a new kernel function is proposed to improve LS-SVM, which can solve the problem of kernel function parameters selection. Further, Lagrange parameters are processed by feature weighting method, and the weighted average value of the Lagrange parameters is calculated as the threshold value of Pruning algorithm, which can solve sparsity problem in LS-SVM. Thirdly, Energy features are inputted into the new LS-SVM to recognize machinery faults according to LS-SVM parameters. Finally, the performance of the proposed method was verified by a fault diagnosis case in a rolling element bearing. The results indicated that this new method could judge and classify the multi-fault of rotating machinery quickly and effectively.
Keywords: Fault diagnosis for rotating machinery, Local Mean Decomposition, morphological filtering, Least Square Support Vector Machine
