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Shock and Vibration
Volume 2016 (2016), Article ID 5836717, 12 pages
Research Article

Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

1School of Mechanical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
2Faculty of Engineering, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang, Malaysia
3School of Mechanical Engineering, Qingdao Technological University, 777 Jialingjian Road, Qingdao 266520, China

Received 10 March 2015; Revised 26 August 2015; Accepted 26 August 2015

Academic Editor: Mohammad Elahinia

Copyright © 2016 Shuang Pan 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.


An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.