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Shock and Vibration
Volume 2017, Article ID 6106103, 9 pages
https://doi.org/10.1155/2017/6106103
Research Article

A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis

School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China

Correspondence should be addressed to Liqin Wang; nc.ude.tih@gnawql

Received 2 August 2016; Revised 9 November 2016; Accepted 4 December 2016; Published 19 February 2017

Academic Editor: Angelo M. Tusset

Copyright © 2017 Yunfeng 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, “Fault analysis of the wear fault development in rolling bearings,” Engineering Failure Analysis, vol. 57, pp. 470–482, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Prabhakar, A. S. Sekhar, and A. R. Mohanty, “Detection and monitoring of cracks in a rotor-bearing system using wavelet transforms,” Mechanical Systems and Signal Processing, vol. 15, no. 2, pp. 447–450, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. 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
  4. N. Tandon and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology International, vol. 32, no. 8, pp. 469–480, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Fault diagnosis of ball bearings using continuous wavelet transform,” Applied Soft Computing, vol. 11, no. 2, pp. 2300–2312, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. C. T. Yiakopoulos and I. A. Antoniadis, “Wavelet based demodulation of vibration signals generated by defects in rolling element bearings,” Shock and Vibration, vol. 9, no. 6, pp. 293–306, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. 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
  8. X. Zhang, J. Kang, L. Xiao, J. Zhao, and H. Teng, “A new improved Kurtogram and its application to bearing fault diagnosis,” Shock and Vibration, vol. 2015, Article ID 385412, 22 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Liu, W. Huang, S. Wang, and Z. Zhu, “Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection,” Signal Processing, vol. 96, pp. 118–124, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. R. F. Dwyer, “A technique for improving detection and estimation of signals contaminated by under ice noise,” Journal of the Acoustical Society of America, vol. 74, no. 1, pp. 124–130, 1983. View at Publisher · View at Google Scholar · View at Scopus
  11. R. F. Dwyer, “Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals,” IEEE Journal of Oceanic Engineering, vol. 9, no. 2, pp. 85–92, 1984. View at Publisher · View at Google Scholar · View at Scopus
  12. R. F. Dwyer, “Detection of non-Gaussian signals by frequency domain kurtosis estimation,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '83), pp. 607–610, Boston, Mass, USA, 1983. View at Scopus
  13. J. Antoni and R. B. Randall, “The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines,” Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 308–331, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Antoni, “Fast computation of the kurtogram for the detection of transient faults,” Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 108–124, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Ding, Z.-D. Huang, and H.-B. Lin, “A weak fault diagnosis method for rolling element bearings based on Morlet wavelet and spectral kurtosis,” Journal of Vibration Engineering, vol. 27, no. 1, pp. 128–135, 2014. View at Google Scholar · View at Scopus
  16. N. Sawalhi and R. B. Randall, “Spectral kurtosis optimization for rolling element bearings,” in Proceedings of the 8th International Symposium on Signal Processing and Its Applications (ISSPA '05), vol. 2, pp. 839–842, IEEE, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. Q. Li and C. Wang, Numerical Analysis, Tsinghua University Press, Beijing, China, 4th edition, 2001.
  18. J. Antoni, “The spectral kurtosis: a useful tool for characterising non-stationary signals,” Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 282–307, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Guo, H. Zheng, Y. Gao, and T. Wu, “The spectral envelope of rolling bearing analysis based on kurtosis,” Journal of Vibration, Measurement & Diagnosis, no. 4, pp. 517–539, 2011. View at Google Scholar
  20. W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study,” Mechanical Systems and Signal Processing, vol. 64-65, pp. 100–131, 2015. View at Publisher · View at Google Scholar · View at Scopus