An intelligent fault identification method rolling bearing sbased on LSSVM optimized by improved PSO.pdf

Contents lists available at SciVerse ScienceDirect Mechanical Systems and Signal Processing Mechanical Systems and Signal Processing 35 (2013) 167–1750888-32 http://d n Corr E-mjournal homepage: /locate/ymsspAn intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSOHongbo Xu, Guohua Chen n School of Mechanical and Automotive Engineering, South China University of Technology, 381 Wushan Road, Guangzhou 510640, Chinaa r t i c l e i n f o Article history: Received 28 November 2011 Received in revised form 10 August 2012 Accepted 2 September 2012 Available online 27 September 2012 Keywords: LSSVM IPSO IMF Energy entropy index Fault identification70/$ - see front matter 2012 Elsevier Ltd. A /10.1016/j.ymssp.2012.09.005 esponding author. Tel./fax: t862022236321 ail address: mmghchen@ (G. Chena b s t r a c t This paper presents an intelligent fault identification method of rolling bearings based on least squares support vector machine optimized by improved particle swarm optimization (IPSO-LSSVM). The method adopts a modified PSO algorithm to optimize the parameters of LSSVM, and then the optimized model could be established to identify the different fault patterns of rolling bearings. Firstly, original fault vibration signals are decomposed into some stationary intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method and the energy feature indexes extrac- tion based on IMF energy entropy is analyzed in detail. Secondly, the extracted energy indexes serve as the fault feature vectors to be input to the IPSO-LSSVM classifier for identifying different fault patterns. Finally, a case study on rolling bearing fault identification demonstrates that the method can effectively enhance identification accuracy and convergence rate. 2012 Elsevier Ltd. All rights reserved.1. Introduction Rolling bearings are mostly used as rotor supports, which appear in almost 90% of rotating machines that find widespread in industri