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International Journal of Rotating Machinery
Volume 2017, Article ID 2598169, 12 pages
https://doi.org/10.1155/2017/2598169
Research Article

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

1Shijiazhuang Mechanical Engineering College, Shijiazhuang 050003, China
2Shanghai Maritime University, Shanghai 200135, China

Correspondence should be addressed to Hongru Li; moc.uhos@861rhil

Received 28 April 2017; Revised 23 May 2017; Accepted 5 September 2017; Published 12 October 2017

Academic Editor: Grzegorz M. Krolczyk

Copyright © 2017 Yaolong 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.

Linked References

  1. I. El-Thalji and E. Jantunen, “A summary of fault modelling and predictive health monitoring of rolling element bearings,” Mechanical Systems and Signal Processing, vol. 60-61, pp. 252–272, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Caesarendra, B. Kosasih, A. K. Tieu, H. Zhu, C. A. S. Moodie, and Q. Zhu, “Acoustic emission-based condition monitoring methods: review and application for low speed slew bearing,” Mechanical Systems and Signal Processing, vol. 72-73, pp. 134–159, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Iwai, T. Honda, T. Miyajima, S. Yoshinaga, M. Higashi, and Y. Fuwa, “Quantitative estimation of wear amounts by real time measurement of wear debris in lubricating oil,” Tribology International, vol. 43, no. 1-2, pp. 388–394, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Taylor and K. Wiggins, “Diagnosis of bearing defects using a heterodyning ultrasound detector,” Journal of Failure Analysis and Prevention, vol. 15, no. 4, pp. 470–473, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Boškoski, M. Gašperin, D. Petelin, and Đ. Juričić, “Bearing fault prognostics using Rényi entropy based features and Gaussian process models,” Mechanical Systems and Signal Processing, vol. 52-53, pp. 327–337, 2015. View at Publisher · View at Google Scholar
  6. R. Q. Yan, Y. B. Liu, and R. X. Gao, “Permutation entropy: a nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, vol. 29, no. 5, pp. 474–484, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Li, Y. Wang, B. Wang, J. Sun, and Y. Li, “The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing,” Mechanical Systems and Signal Processing, vol. 82, no. 1, pp. 490–502, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Pan and W. Tsao, “Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings,” International Journal of Mechanical Sciences, vol. 69, pp. 114–124, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. 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
  10. 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
  11. R. K. Tripathy, L. N. Sharma, and S. Dandapat, “Detection of shockable ventricular arrhythmia using variational mode decomposition,” Journal of Medical Systems, vol. 40, no. 4, article no. 79, pp. 1–13, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Lahmiri, “Intraday stock price forecasting based on variational mode decomposition,” Journal of Computational Science, vol. 12, pp. 23–27, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Zhang, K. Liu, L. Qin, and X. An, “Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods,” Energy Conversion and Management, vol. 112, pp. 208–219, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. A. A. Abdoos, P. K. Mianaei, and M. R. Ghadikolaei, “Combined VMD-SVM based feature selection method for classification of power quality events,” Applied Soft Computing, vol. 38, pp. 637–646, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Zhu, C. Wang, Z. Hu, F. Kong, and X. Liu, “Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 231, no. 4, pp. 635–654, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Tang and X. Wang, “Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing,” Journal of Xi'an Jiaotong University, vol. 49, no. 5, pp. 73–81, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Pan, J. Chen, and X. Li, “Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means,” Mechanical Systems and Signal Processing, vol. 24, no. 2, pp. 559–566, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Wang, H.-R. Li, Q.-H. Chen, and B.-H. Xu, “Rolling bearing performance degradative state recognition based on mathematical morphological fractal dimension and fuzzy center means,” Acta Armamentarii, vol. 36, no. 10, pp. 1982–1990, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Qiu, J. Lee, J. Lin, and G. Yu, “Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics,” Journal of Sound and Vibration, vol. 289, no. 4-5, pp. 1066–1090, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Williams, X. Ribadeneira, S. Billington, and T. Kurfess, “Rolling element bearing diagnostics in run-to-failure lifetime testing,” Mechanical Systems and Signal Processing, vol. 15, no. 5, pp. 979–993, 2001. View at Publisher · View at Google Scholar · View at Scopus
  21. I. El-Thalji and E. Jantunen, “A descriptive model of wear evolution in rolling bearings,” Engineering Failure Analysis, vol. 45, pp. 204–224, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Himberg, K. Korpiaho, H. Mannila, J. Tikanmäki, and H. T. T. Toivonen, “Time series segmentation for context recognition in mobile devices,” in Proceedings of the 1st IEEE International Conference on Data Mining (ICDM '01), pp. 203–210, December 2001. View at Scopus
  23. A. Kehagias, E. Nidelkou, and V. Petridis, “A dynamic programming segmentation procedure for hydrological and environmental time series,” Stochastic Environmental Research and Risk Assessment (SERRA), vol. 20, no. 1-2, pp. 77–94, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. A. Gedikli, H. Aksoy, N. E. Unal, and A. Kehagias, “Modified dynamic programming approach for offline segmentation of long hydrometeorological time series,” Stochastic Environmental Research and Risk Assessment, vol. 24, no. 5, pp. 547–557, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Abonyi, B. Feil, S. Nemeth, and P. Arva, “Modified Gath-GEVa clustering for fuzzy segmentation of multivariate time-series,” Fuzzy Sets and Systems. An International Journal in Information Science and Engineering, vol. 149, no. 1, pp. 39–56, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. P. Nectoux, R. Gouriveau, and K. Medjaher, “An experimental platform for bearings accelerated life test,” in Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, USA, 2012.