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Shock and Vibration
Volume 2016, Article ID 8729572, 11 pages
http://dx.doi.org/10.1155/2016/8729572
Research Article

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

School of Electrical, Electronics and Computer Engineering, University of Ulsan, Building No. 7, Room No. 308, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea

Received 2 September 2016; Accepted 17 November 2016

Academic Editor: Minvydas Ragulskis

Copyright © 2016 Sheraz Ali Khan and Jong-Myon Kim. 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 [3 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
  • Ge Xin, Nacer Hamzaoui, and Jerome Antoni, “Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals,” Measurement, 2018. View at Publisher · View at Google Scholar
  • Jee Siang, M.H. Lim, and M. Salman Leong, “Review of vibration-based energy harvesting technology: Mechanism and architectural approach,” International Journal of Energy Research, 2018. View at Publisher · View at Google Scholar