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International Journal of Rotating Machinery
Volume 2017, Article ID 2384184, 10 pages
https://doi.org/10.1155/2017/2384184
Research Article

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

1School of Urban Rail Transportation, Soochow University, Suzhou 215131, China
2School of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, China

Correspondence should be addressed to Changqing Shen; nc.ude.adus@nehsqc

Received 2 December 2016; Revised 18 February 2017; Accepted 23 February 2017; Published 13 March 2017

Academic Editor: Pavan K. Kankar

Copyright © 2017 Jun Shuai 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 [3 citations]

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

  • Xiaojiao Gu, and Changzheng Chen, “Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD,” International Journal of Rotating Machinery, vol. 2017, pp. 1–12, 2017. View at Publisher · View at Google Scholar
  • Levent Eren, Turker Ince, and Serkan Kiranyaz, “A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier,” Journal of Signal Processing Systems, 2018. View at Publisher · View at Google Scholar
  • Abrham Debasu Mengistu, “The effects of segmentation techniques in digital image based identification of Ethiopian coffee variety,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 2, pp. 713–717, 2018. View at Publisher · View at Google Scholar