Fault diagnosis for rotating machinery based on Local Mean Decomposition morphology filtering and Least Square Support Vector Machine

• Published in: Journal of intelligent & fuzzy systems Vol. 32; no. 3; pp. 2061 - 2070
• Main Authors: Xu, Tongle, Yin, Zhaojie, Cai, Daoyong, Zheng, Diankun
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2017
• Subjects: Decomposition; Fault diagnosis; Faults; Filtration; Kernel functions; Least squares; Machinery; Morphology; Pruning; Rotating machinery; Support vector machines
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-161676

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.