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Shock and Vibration
Volume 2017, Article ID 2623081, 9 pages
https://doi.org/10.1155/2017/2623081
Research Article

The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

1School of Basic Sciences, Changchun University of Technology, Changchun 130012, China
2Graduate School, Changchun University of Technology, Changchun 130012, China

Correspondence should be addressed to Xiaogang Dong; nc.ude.tucc@gnagoaixgnod

Received 9 May 2017; Accepted 2 July 2017; Published 20 August 2017

Academic Editor: Simone Cinquemani

Copyright © 2017 Xiwen Qin 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
  • Alexander Prosvirin, Manjurul Islam, Jaeyoung Kim, and Jong-Myon Kim, “Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models,” Sensors, vol. 18, no. 7, pp. 2040, 2018. View at Publisher · View at Google Scholar
  • Zhijie Zhou, Xiaoxia Han, Bangcheng Zhang, Zhanli Wang, Guanyu Hu, and Xiaojing Yin, “A new health estimation model for CNC machine tool based on infinite irrelevance and belief rule base,” Microelectronics Reliability, vol. 84, pp. 187–196, 2018. View at Publisher · View at Google Scholar
  • Guiji Tang, Bin Pang, Tian Tian, and Chong Zhou, “Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest,” Applied Sciences, vol. 8, no. 10, pp. 1859, 2018. View at Publisher · View at Google Scholar