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Shock and Vibration
Volume 2014 (2014), Article ID 154291, 8 pages
http://dx.doi.org/10.1155/2014/154291
Research Article

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

1State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
2College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China

Received 10 September 2013; Accepted 19 February 2014; Published 3 March 2014

Academic Editor: Valder Steffen

Copyright © 2014 Jinde Zheng 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. D. Yu, J. Cheng, and Y. Yang, “Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings,” Mechanical Systems and Signal Processing, vol. 19, no. 2, pp. 259–270, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Sejdić, I. Djurović, and J. Jiang, “Time-frequency feature representation using energy concentration: an overview of recent advances,” Digital Signal Processing, vol. 19, no. 1, pp. 153–183, 2009. View at Google Scholar
  3. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A, vol. 454, no. 1971, pp. 903–995, 1998. View at Google Scholar · View at Scopus
  4. Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society A, vol. 460, no. 2046, pp. 1597–1611, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Yan and R. X. Gao, “Approximate entropy as a diagnostic tool for machine health monitoring,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 824–839, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Logan and J. Mathew, “Using the correlation dimension for vibration fault diagnosis of rolling element bearings—I. Basic concepts,” Mechanical Systems and Signal Processing, vol. 10, no. 3, pp. 241–250, 1996. View at Publisher · View at Google Scholar · View at Scopus
  7. J. D. Jiang, J. Chen, and L. S. Qu, “The application of correlation dimension in gearbox condition monitoring,” Journal of Sound and Vibration, vol. 223, no. 4, pp. 529–541, 1999. View at Google Scholar · View at Scopus
  8. L. Zhang, G. Xiong, H. Liu, H. Zou, and W. Guo, “Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference,” Expert Systems with Applications, vol. 37, no. 8, pp. 6077–6085, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate and sample entropy,” American Journal of Physiology—Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000. View at Google Scholar · View at Scopus
  10. Y.-H. Pan, Y.-H. Wang, S.-F. Liang, and K.-T. Lee, “Fast computation of sample entropy and approximate entropy in biomedicine,” Computer Methods and Programs in Biomedicine, vol. 104, no. 3, pp. 382–396, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Physical Review Letters, vol. 89, no. 6, Article ID 068102, 4 pages, 2002. View at Google Scholar · View at Scopus
  12. M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E, vol. 71, Article ID 021906, 2005. View at Google Scholar
  13. W. Aziz and M. Arif, “Multiscale permutation entropy of physiological time series,” in Proceedings of the 9th International Multitopic Conference (INMIC '05), December 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, Article ID 174102, 4 pages, 2002. View at Google Scholar · View at Scopus
  15. C. Bandt, G. Keller, and B. Pompe, “Entropy of interval maps via permutations,” Nonlinearity, vol. 15, no. 5, pp. 1595–1602, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Yan, Y. Liu, and R. X. Gao, “Permutation entropy: a nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, vol. 29, pp. 474–484, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Nicolaou and J. Georgiou, “Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines,” Expert Systems with Applications, vol. 39, no. 1, pp. 202–209, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” in Advances in Neural Information Processing System, MIT Press, Cambridge, Mass, USA, 2005. View at Google Scholar
  19. Y. Yang, D. Yu, and J. Cheng, “A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,” Measurement, vol. 40, no. 9-10, pp. 943–950, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Applied Soft Computing Journal, vol. 11, no. 6, pp. 4203–4211, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Cheng, D. Yu, J. Tang, and Y. Yang, “Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery,” Shock and Vibration, vol. 16, no. 1, pp. 89–98, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Cao, W.-W. Tung, J. B. Gao, V. A. Protopopescu, and L. M. Hively, “Detecting dynamical changes in time series using the permutation entropy,” Physical Review E, vol. 70, no. 4, Article ID 046217, 7 pages, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Lei, Z. He, and Y. Zi, “A new approach to intelligent fault diagnosis of rotating machinery,” Expert Systems with Applications, vol. 35, no. 4, pp. 1593–1600, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Fault diagnosis of ball bearings using machine learning methods,” Expert Systems with Applications, vol. 38, no. 3, pp. 1876–1886, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Qin, Y. Shen, J. Zhu, and H. Xu, “Dynamic analysis of hydrodynamic bearing-rotor system based on neural network,” International Journal of Engineering Science, vol. 43, no. 5-6, pp. 520–531, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. Yang, D. Yu, and J. Cheng, “A roller bearing fault diagnosis method based on EMD energy entropy and ANN,” Journal of Sound and Vibration, vol. 294, no. 1-2, pp. 269–277, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. S.-W. Fei and X.-B. Zhang, “Fault diagnosis of power transformer based on support vector machine with genetic algorithm,” Expert Systems with Applications, vol. 36, no. 8, pp. 11352–11357, 2009. View at Publisher · View at Google Scholar · View at Scopus