Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 293878, 8 pages
Research Article

A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

1School of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2School of Automation, Chongqing University, Chongqing 400044, China
3The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China
4Chongqing Academy of Metrology and Quality Inspection, Chongqing 401121, China
5School of Mechanical and Electronic Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
6Mechanical and Electrical Engineering Department, Chongqing Vocational Institute of Safety & Technology, Wanzhou, Chongqing 404020, China

Received 23 April 2014; Revised 8 June 2014; Accepted 15 June 2014; Published 8 July 2014

Academic Editor: Ruqiang Yan

Copyright © 2014 Shaojiang Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.