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Shock and Vibration
Volume 2014, Article ID 717465, 15 pages
http://dx.doi.org/10.1155/2014/717465
Research Article

Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

1School of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China
3Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang’an University, Xi’an 710021, China

Received 15 March 2013; Accepted 5 August 2013; Published 5 March 2014

Academic Editor: Valder Steffen

Copyright © 2014 Shaojiang Dong 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.

  • Divya Sardana, Raj Bhatnagar, Radu Pavel, and Jon Iverson, “Data driven predictive analytics for a spindle's health,” 2015 IEEE International Conference on Big Data (Big Data), pp. 1378–1387, . View at Publisher · View at Google Scholar
  • Ran Zhang, Lifeng Wu, Xiaohui Fu, and Beibei Yao, “Classification of bearing data based on deep belief networks,” 2016 Prognostics and System Health Management Conference (PHM-Chengdu), pp. 1–6, . View at Publisher · View at Google Scholar
  • Ling Wang, Hong Xu, Xiai Chen, Wenbo Na, Changjun Chen, and Jing Pan, “Prediction of remaining useful life for electrical sliding plug door of metro vehicles,” Zhongguo Jixie Gongcheng/China Mechanical Engineering, vol. 27, no. 22, pp. 2994–3000, 2016. View at Publisher · View at Google Scholar
  • Yan Zhang, Baoping Tang, Yan Han, and Lei Deng, “Bearing performance degradation assessment based on time-frequency code features and SOM network,” Measurement Science and Technology, vol. 28, no. 4, pp. 045601, 2017. View at Publisher · View at Google Scholar
  • Xiaoyang Chen, Luosheng Qin, and Xuejin Shen, “Reliability assessment of bearings based on competing failure under small sample data,” Zhendong yu Chongji/Journal of Vibration and Shock, vol. 36, no. 23, pp. 248–254, 2017. View at Publisher · View at Google Scholar
  • Yang Feng, Jie Chen, Xiao-diao Huang, and Rong-jing Hong, “Online residual useful life prediction of large-size slewing bearings—A data fusion method,” Journal of Central South University, vol. 24, no. 1, pp. 114–126, 2017. View at Publisher · View at Google Scholar
  • Luosheng Qin, Xuejin Shen, Xiaoyang Chen, and Pandong Gao, “Reliability assessment of bearings based on performance degradation values under small samples,” Strojniski Vestnik/Journal of Mechanical Engineering, vol. 63, no. 4, pp. 248–254, 2017. View at Publisher · View at Google Scholar
  • Bo Wu, Wei Li, and Ming-quan Qiu, “Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator,” Shock and Vibration, vol. 2017, pp. 1–10, 2017. View at Publisher · View at Google Scholar
  • Dileep Kumar Appana, Md. Rashedul Islam, and Jong-Myon Kim, “Reliable fault diagnosis of bearings using distance and density similarity on an Enhanced k-NN,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10142, pp. 193–203, 2017. View at Publisher · View at Google Scholar
  • Gabriel Pino, José Roberto Ribas, and Luciana Fernandes Guimarães, “Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves,” Shock and Vibration, vol. 2018, pp. 1–12, 2018. View at Publisher · View at Google Scholar
  • Qing Li, and Steven Y. Liang, “Degradation Trend Prognostics for Rolling Bearing Using Improved R/S Statistic Model and Fractional Brownian Motion Approach,” IEEE Access, vol. 6, pp. 21103–21114, 2018. View at Publisher · View at Google Scholar