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Shock and Vibration
Volume 2016, Article ID 9306205, 9 pages
http://dx.doi.org/10.1155/2016/9306205
Research Article

Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

Jie Tao,1,2 Yilun Liu,1,3 and Dalian Yang1,4

1School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
2Key Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, China
3Light Alloy Research Institute, Central South University, Changsha 410083, China
4Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, China

Received 20 April 2016; Revised 3 August 2016; Accepted 11 August 2016

Academic Editor: Ganging Song

Copyright © 2016 Jie Tao 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 [7 citations]

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

  • Yi Chai, Qiu Tang, Hao Ren, Jian-Feng Qu, and Xin Ye, “Deep learning for fault diagnosis: The state of the art and challenge,” Kongzhi yu Juece/Control and Decision, vol. 32, no. 8, pp. 1345–1358, 2017. View at Publisher · View at Google Scholar
  • Aditya Sharma, Sharad Bhardwaj, and Pavan Kumar Kankar, “Fault diagnosis of rolling element bearings using fractional linear prediction and AI techniques,” Life Cycle Reliability and Safety Engineering, 2018. View at Publisher · View at Google Scholar
  • Samir Khan, and Takehisa Yairi, “A review on the application of deep learning in system health management,” Mechanical Systems and Signal Processing, vol. 107, pp. 241–265, 2018. View at Publisher · View at Google Scholar
  • Xuefeng Chen, Shibin Wang, Baijie Qiao, and Qiang Chen, “Basic research on machinery fault diagnostics: Past, present, and future trends,” Frontiers of Mechanical Engineering, vol. 13, no. 2, pp. 264–291, 2018. View at Publisher · View at Google Scholar
  • Guangquan Zhao, Guangxing Niu, Cong Hu, Bin Zhang, Xiaoyong Liu, and Yuefeng Liu, “A novel approach for analog circuit fault diagnosis based on Deep Belief Network,” Measurement: Journal of the International Measurement Confederation, vol. 121, pp. 170–178, 2018. View at Publisher · View at Google Scholar
  • Chih-Wen Chang, Hau-Wei Lee, and Chein-Hung Liu, “A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools,” Inventions, vol. 3, no. 3, pp. 41, 2018. View at Publisher · View at Google Scholar
  • Duy-Tang Hoang, and Hee-Jun Kang, “A Survey on Deep Learning based Bearing Fault Diagnosis,” Neurocomputing, 2018. View at Publisher · View at Google Scholar