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Shock and Vibration
Volume 2016, Article ID 5132046, 11 pages
http://dx.doi.org/10.1155/2016/5132046
Research Article

Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Received 29 January 2016; Revised 14 May 2016; Accepted 13 June 2016

Academic Editor: Nuno M. Maia

Copyright © 2016 Te Han and Dongxiang Jiang. 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 [7 citations]

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

  • Te Han, Dong Xiang Jiang, and Wen Guang Yang, “Degradation State Assessment of Rolling Bearing Based on Variational Mode Decomposition and Energy Distribution,” Key Engineering Materials, vol. 754, pp. 371–374, 2017. View at Publisher · View at Google Scholar
  • Jie Liu, Youmin Hu, Bo Wu, Yan Wang, and Fengyun Xie, “A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings,” Sensors, vol. 17, no. 6, pp. 1143, 2017. View at Publisher · View at Google Scholar
  • Te Han, Dongxiang Jiang, Xiaochen Zhang, and Yankui Sun, “Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition,” Sensors, vol. 17, no. 4, pp. 689, 2017. View at Publisher · View at Google Scholar
  • Jaewon Sa, Younchang Choi, Yongwha Chung, Jonguk Lee, and Daihee Park, “Aging Detection of Electrical Point Machines Based on Support Vector Data Description,” Symmetry, vol. 9, no. 12, pp. 290, 2017. View at Publisher · View at Google Scholar
  • Xiwen Qin, Qiaoling Li, Xiaogang Dong, and Siqi Lv, “The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest,” Shock and Vibration, vol. 2017, pp. 1–9, 2017. View at Publisher · View at Google Scholar
  • Yizhou Yang, and Dongxiang Jiang, “Casing Vibration Fault Diagnosis Based on Variational Mode Decomposition, Local Linear Embedding, and Support Vector Machine,” Shock and Vibration, vol. 2017, pp. 1–14, 2017. View at Publisher · View at Google Scholar
  • Te Han, Dongxiang Jiang, Yankui Sun, Nanfei Wang, and Yizhou Yang, “Intelligent Fault Diagnosis Method for Rotating Machinery via Dictionary Learning and Sparse Representation-Based Classification,” Measurement, 2018. View at Publisher · View at Google Scholar