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Journal of Sensors
Volume 2017, Article ID 8092691, 15 pages
https://doi.org/10.1155/2017/8092691
Research Article

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xiaochen Zhang; moc.liamtoh@8002hcxgnahz

Received 17 January 2017; Revised 27 June 2017; Accepted 20 September 2017; Published 22 October 2017

Academic Editor: Pietro Siciliano

Copyright © 2017 Xiaochen Zhang 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.

Linked References

  1. Y. Lei, J. Lin, Z. He, and M. J. Zuo, “A review on empirical mode decomposition in fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 35, no. 1-2, pp. 108–126, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Yang, D. Xiang, A. Bryant, P. Mawby, L. Ran, and P. Tavner, “Condition monitoring for device reliability in power electronic converters: A review,” IEEE Transactions on Power Electronics, vol. 25, no. 11, pp. 2734–2752, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. R. B. Randall and J. Antoni, “Rolling element bearing diagnostics—a tutorial,” Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 485–520, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Wang, R. Li, G. Tang, H. Yuan, Q. Zhao, and X. Cao, “A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition,” PLoS ONE, vol. 9, no. 10, article e109166, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. P. Feng, M. Liang, Y. Zhang, and S. M. Hou, “Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation,” Journal of Renewable Energy, vol. 47, pp. 112–126, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Y. Wu, J. C. Chen, and C. C. Wang, “Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization,” Mechanical Systems and Signal Processing, vol. 30, pp. 103–122, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Singh and N. Kumar, “Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition,” Journal of Mechanical Science and Technology, vol. 28, no. 12, pp. 4869–4876, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Li, R. Zhou, Q. Hu, and X. Liu, “Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine,” Mechanical Systems and Signal Processing, vol. 28, pp. 608–621, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Zhang and J. Zhou, “Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines,” Mechanical Systems and Signal Processing, vol. 41, no. 1-2, pp. 127–140, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. X.-M. Tao, D.-X. Zhang, S.-Y. Hao, and D.-D. Fu, “SVM classifier for unbalanced data based on spectrum cluster-based under-sampling approaches,” Control and Decision, vol. 27, no. 12, pp. 1761–1768, 2012. View at Google Scholar · View at Scopus
  11. H. Yi, X. F. Song, B. Jiang, Y. F. Liu, and Z. H. Zhou, “Fault diagnosis based on self-tuning support vector machine in sample unbalance condition,” Transactions of Beijing Institute of Technology, vol. 33, no. 4, pp. 394–398, 2013. View at Google Scholar
  12. Y. Zhang, X. Zhou, H. Shi, Z. Zheng, and S. Li, “Corrosion pitting damage detection of rolling bearings using data mining techniques,” International Journal of Modelling, Identification and Control, vol. 24, no. 3, pp. 235–243, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Lan, W. Zong, X. Ding et al., “A two-step fault diagnosis framework for rolling element bearings with imbalanced data,” in Proceedings of the 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016, pp. 620–625, Xian, China, August 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognition Letters, vol. 31, no. 8, pp. 651–666, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. S. M. Razavi Zadegan, M. Mirzaie, and F. Sadoughi, “Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets,” Knowledge-Based Systems, vol. 39, pp. 133–143, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Laio and A. Rodriguez, “Clustering by fast search and find of density peaks,” Science, vol. 344, no. 6191, pp. 1492–1496, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Xie, H. Gao, W. Xie, X. Liu, and P. W. Grant, “Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors,” Information Sciences, vol. 354, pp. 19–40, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, “A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 4, pp. 463–484, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. V. López, A. Fernández, S. García, V. Palade, and F. Herrera, “An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics,” Information Sciences, vol. 250, pp. 113–141, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. Y. Wang and R. Markert, “Filter bank property of variational mode decomposition and its applications,” Signal Processing, vol. 120, pp. 509–521, 2016. View at Publisher · View at Google Scholar · View at Scopus