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Shock and Vibration
Volume 2015, Article ID 512163, 9 pages
http://dx.doi.org/10.1155/2015/512163
Research Article

The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

1College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received 24 May 2014; Revised 12 November 2014; Accepted 20 November 2014

Academic Editor: Gyuhae Park

Copyright © 2015 Jiyong Li 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 [2 citations]

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

  • Ruqiang Yan, Mengxiao Shan, Jianwei Cui, and Yahui Wu, “Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis,” Shock and Vibration, vol. 2015, pp. 1–9, 2015. View at Publisher · View at Google Scholar
  • Jun-hong Zhang, and Yu Liu, “Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines,” Frontiers of Information Technology & Electronic Engineering, vol. 18, no. 2, pp. 272–286, 2017. View at Publisher · View at Google Scholar