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Shock and Vibration
Volume 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 [11 citations]

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

  • Shuzhi Dong, Zhifen Zhang, Gurangrui Wen, Shuzhi Dong, Zhifen Zhang, and Guangrui Wen, “Design and application of unsupervised convolutional neural networks integrated with deep belief networks for mechanical fault diagnosis,” 2017 Prognostics and System Health Management Conference (PHM-Harbin), pp. 1–7, . View at Publisher · View at Google Scholar
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