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Journal of Sensors
Volume 2017, Article ID 8092691, 15 pages
Research Article

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xiaochen Zhang; moc.liamtoh@8002hcxgnahz

Received 17 January 2017; Revised 27 June 2017; Accepted 20 September 2017; Published 22 October 2017

Academic Editor: Pietro Siciliano

Copyright © 2017 Xiaochen Zhang 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 [4 citations]

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

  • Muhammad Sohaib, Cheol-Hong Kim, and Jong-Myon Kim, “A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis,” Sensors, vol. 17, no. 12, pp. 2876, 2017. View at Publisher · View at Google Scholar
  • Caleb Vununu, Kwang-Seok Moon, Suk-Hwan Lee, and Ki-Ryong Kwon, “A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals,” Sensors, vol. 18, no. 8, pp. 2634, 2018. View at Publisher · View at Google Scholar
  • B. Richhariya, and M. Tanveer, “A robust fuzzy least squares twin support vector machine for class imbalance learning,” Applied Soft Computing, 2018. View at Publisher · View at Google Scholar
  • Xiaoguang Zhang, Zhenyue Song, Dandan Li, Wei Zhang, Zhike Zhao, and Yingying Chen, “Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR,” Shock and Vibration, vol. 2018, pp. 1–13, 2018. View at Publisher · View at Google Scholar