Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2016 (2016), Article ID 6127479, 12 pages
http://dx.doi.org/10.1155/2016/6127479
Research Article

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

1School of Reliability and Systems Engineering, Beihang University, Beijing, China
2Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing, China

Received 26 April 2016; Accepted 20 July 2016

Academic Editor: Fiorenzo A. Fazzolari

Copyright © 2016 Hongmei Liu 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 [6 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
  • Carlos Mateo, and Juan Antonio Talavera, “Short-Time Fourier Transform with the Window Size Fixed in the Frequency Domain,” Digital Signal Processing, 2017. View at Publisher · View at Google Scholar
  • Funa Zhou, Yulin Gao, and Chenglin Wen, “A Novel Multimode Fault Classification Method Based on Deep Learning,” Journal of Control Science and Engineering, vol. 2017, pp. 1–14, 2017. View at Publisher · View at Google Scholar
  • David Verstraete, Andrés Ferrada, Enrique López Droguett, Viviana Meruane, and Mohammad Modarres, “Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings,” Shock and Vibration, vol. 2017, pp. 1–17, 2017. View at Publisher · View at Google Scholar
  • Haodong Yuan, Jin Chen, and Guangming Dong, “Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM,” Mathematical Problems in Engineering, vol. 2017, pp. 1–16, 2017. 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