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Shock and Vibration
Volume 2016, Article ID 6748469, 10 pages
Research Article

Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

1EPC Elektroprivreda BiH, Kreka Coal Mines, 75000 Tuzla, Bosnia and Herzegovina
2Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
3Bruel and Kjær Vibro, 2850 Nærum, Denmark
4Technical University of Denmark, 2800 Lyngby, Denmark

Received 1 July 2015; Revised 17 September 2015; Accepted 27 September 2015

Academic Editor: Chao Tao

Copyright © 2016 Rusmir Bajric 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 [4 citations]

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

  • A. Romero, Y. Lage, S. Soua, B. Wang, and T.-H. Gan, “Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System,” Shock and Vibration, vol. 2016, pp. 1–18, 2016. View at Publisher · View at Google Scholar
  • Hongfang Chen, Yanqiang Sun, Zhaoyao Shi, and Jiachun Lin, “Intelligent Analysis Method of Gear Faults Based on FRWT and SVM,” Shock and Vibration, vol. 2016, pp. 1–9, 2016. View at Publisher · View at Google Scholar
  • Indhu Ramalingam, Sankaran Rangasamy Annamalai, and Sugumaran Vaithiyanathan, “Fault diagnosis of wind turbine bearing using wireless sensor networks,” Thermal Science, vol. 21, pp. 523–531, 2017. View at Publisher · View at Google Scholar
  • Miao He, David He, Jae Yoon, Thomas J Nostrand, Junda Zhu, and Eric Bechhoefer, “Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, pp. 1748006X1876870, 2018. View at Publisher · View at Google Scholar