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Shock and Vibration
Volume 2016, Article ID 5836717, 12 pages
http://dx.doi.org/10.1155/2016/5836717
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.

Citations to this Article [11 citations]

The following is the list of published articles that have cited the current article.

  • Ann Smith, Fengshou Gu, and Andrew Ball, “Maintaining model efficiency, avoiding bias and reducing input parameter volume in compressor fault classification,” 2016 7th International Conference on Mechanical and Aerospace Engineering (ICMAE), pp. 196–201, . View at Publisher · View at Google Scholar
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  • Dengyu Xiao, Yixiang Huang, Chengjin Qin, Haotian Shi, and Yanming Li, “Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN,” Shock and Vibration, vol. 2019, pp. 1–14, 2019. View at Publisher · View at Google Scholar