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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7906834, 14 pages
http://dx.doi.org/10.1155/2016/7906834
Research Article

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

1Automatic Research Group, Universidad Tecnológica de Pereira, Pereira, Colombia
2Technological and Environmental Advances Research Group, Universidad Católica de Manizales, Colombia
3Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
4Universidad Nacional de Colombia, Manizales, Colombia

Received 19 February 2016; Revised 10 May 2016; Accepted 8 June 2016

Academic Editor: Weihua Li

Copyright © 2016 Mauricio Holguín-Londoño 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. N. Baydar and A. Ball, “Detection of gear failures via vibration and acoustic signals using wavelet transform,” Mechanical Systems and Signal Processing, vol. 17, no. 4, pp. 787–804, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. D. P. Jena and S. N. Panigrahi, “Automatic gear and bearing fault localization using vibration and acoustic signals,” Applied Acoustics, vol. 98, pp. 20–33, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Henriquez, J. B. Alonso, M. A. Ferrer, and C. M. Travieso, “Review of automatic fault diagnosis systems using audio and vibration signals,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 5, pp. 642–652, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Amarnath and I. R. Praveen Krishna, “Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings,” IET Science, Measurement & Technology, vol. 6, no. 4, pp. 279–287, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Lin, “Feature extraction of machine sound using wavelet and its application in fault diagnosis,” NDT & E International, vol. 34, no. 1, pp. 25–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. 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
  7. M. Amarnath and I. R. Praveen Krishna, “Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis,” Measurement: Journal of the International Measurement Confederation, vol. 58, pp. 154–164, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. 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
  9. Z. K. Peng, P. W. Tse, and F. L. Chu, “A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing,” Mechanical Systems and Signal Processing, vol. 19, no. 5, pp. 974–988, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Ricci and P. Pennacchi, “Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions,” Mechanical Systems and Signal Processing, vol. 25, no. 3, pp. 821–838, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Obuchowski, A. Wyłomańska, and R. Zimroz, “Selection of informative frequency band in local damage detection in rotating machinery,” Mechanical Systems and Signal Processing, vol. 48, no. 1-2, pp. 138–152, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Yan, R. X. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: a review with applications,” Signal Processing, vol. 96, pp. 1–15, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Szabó, “Information theoretical estimators toolbox,” Journal of Machine Learning Research, vol. 15, pp. 283–287, 2014. View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. G. G. Yen and K.-C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 650–667, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  16. J. Ye, “Fault diagnosis of turbine based on fuzzy cross entropy of vague sets,” Expert Systems with Applications, vol. 36, no. 4, pp. 8103–8106, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. E. F. Sierra-Alonso, O. Cardona-Morales, C. D. Acosta-Medina, and G. Castellanos-Dominguez, “Spectral correlation measure for selecting intrinsic mode functions,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 231–238, Springer, Berlin, Germany, 2014. View at Google Scholar
  18. G. L. Forbes and R. B. Randall, “Estimation of turbine blade natural frequencies from casing pressure and vibration measurements,” Mechanical Systems and Signal Processing, vol. 36, no. 2, pp. 549–561, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Wang, M. Gan, and C. Zhu, “Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory,” Journal of Intelligent Manufacturing, pp. 1–15, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. B. S. Anami, V. B. Pagi, and S. M. Magi, “Wavelet-based acoustic analysis for determining health condition of motorized two-wheelers,” Applied Acoustics, vol. 72, no. 7, pp. 464–469, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. 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