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Shock and Vibration
Volume 2015, Article ID 167902, 9 pages
http://dx.doi.org/10.1155/2015/167902
Research Article

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
2Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA

Received 30 March 2015; Accepted 16 July 2015

Academic Editor: Chuan Li

Copyright © 2015 Weigang Wen 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. R. M. Jones, “Enveloping for bearing analysis,” Sound & Vibration, vol. 30, no. 2, pp. 10–15, 1996. View at Google Scholar · View at Scopus
  2. H. Ocak, K. A. Loparo, and F. M. Discenzo, “Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics,” Journal of Sound and Vibration, vol. 302, no. 4-5, pp. 951–961, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Bujoreanu, V. Horga, and B. Dragan, “Vibration analysis methods in bearing damage detection,” Applied Mechanics and Materials, vol. 371, no. 1, pp. 622–626, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. 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
  5. L. Guo, J. Chen, and X. Li, “Rolling bearing fault classification based on envelope spectrum and support vector machine,” Journal of Vibration and Control, vol. 15, no. 9, pp. 1349–1363, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Luan, H. Kang, H. Zheng, Q. Cui, and J. Cao, “Application in fault diagnosis of bearing with order envelope spectrum analysis,” Journal of Vibration, Measurement and Diagnosis, vol. 26, no. 9, pp. 215–217, 2006. View at Google Scholar · View at Scopus
  7. Y. Zhou, J. Chen, G. M. Dong, W. B. Xiao, and Z. Y. Wang, “Wigner-Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 225, no. 12, pp. 2831–2847, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Dong, K. Qi, X. Chen, Y. Zi, Z. He, and B. Li, “Sifting process of EMD and its application in rolling element bearing fault diagnosis,” Journal of Mechanical Science and Technology, vol. 23, no. 8, pp. 2000–2007, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Yang, Y. He, J. Cheng, and D. Yu, “A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach,” Measurement, vol. 42, no. 4, pp. 542–551, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Guo, J. Na, B. Li, and R.-F. Fung, “Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing,” Journal of Sound and Vibration, vol. 333, no. 13, pp. 2983–2994, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. D. Kateris, D. Moshou, X.-E. Pantazi, I. Gravalos, N. Sawalhi, and S. Loutridis, “A machine learning approach for the condition monitoring of rotating machinery,” Journal of Mechanical Science and Technology, vol. 28, no. 1, pp. 61–71, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Ben Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, and F. Fnaiech, “Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals,” Applied Acoustics, vol. 89, no. 3, pp. 16–27, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Fernández-Francos, D. Marténez-Rego, O. Fontenla-Romero, and A. Alonso-Betanzos, “Automatic bearing fault diagnosis based on one-class m-SVM,” Computers and Industrial Engineering, vol. 64, no. 1, pp. 357–365, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Zeng, L. Zhang, and Y. Xiao, “A method combining order tracking and fuzzy c-means for diesel engine fault detection and isolation,” Shock and Vibration. In press.
  15. J. Zheng, J. Cheng, and Y. Yang, “Multiscale permutation entropy based rolling bearing fault diagnosis,” Shock and Vibration, vol. 2014, Article ID 154291, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Caesarendra, B. Kosasih, A. K. Tieu, and C. A. S. Moodie, “Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring,” Mechanical Systems and Signal Processing, vol. 50-51, pp. 116–138, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. S.-D. Wu, P.-H. Wu, C.-W. Wu, J.-J. Ding, and C.-C. Wang, “Bearing fault diagnosis based on multiscale permutation entropy and support vector machine,” Entropy, vol. 14, no. 8, pp. 1343–1356, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. W. J. Wang, Z. T. Wu, and J. Chen, “Fault identification in rotating machinery using the correlation dimension and bispectra,” Nonlinear Dynamics, vol. 25, no. 4, pp. 383–393, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. 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
  20. J. Yang, Y. Zhang, and Y. Zhu, “Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2012–2024, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. C.-H. Chen, R.-J. Shyu, and C.-K. Ma, “A new fault diagnosis method of rotating machinery,” Shock and Vibration, vol. 15, no. 6, pp. 585–598, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. D. B. Logan and J. Mathew, “Using the correlation dimension for vibration fault diagnosis of rolling element bearings—II. Selection of experimental parameters,” Mechanical Systems and Signal Processing, vol. 10, no. 3, pp. 251–264, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. P.-L. Zhang, B. Li, S.-S. Mi, Y.-T. Zhang, and D.-S. Liu, “Bearing fault detection using multi-scale fractal dimensions based on morphological covers,” Shock and Vibration, vol. 19, no. 6, pp. 1373–1383, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Xiong, W. Huang, and L. Zhang, “Fault severity identification of rolling bearing based on multiscale entropy,” Journal of Applied Sciences, vol. 13, no. 13, pp. 2404–2408, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. M. M. Dubovikov, N. V. Starchenko, and M. S. Dubovikov, “Dimension of the minimal cover and fractal analysis of time series,” Physica A, vol. 339, no. 3-4, pp. 591–608, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. A. R. Backes and O. M. Bruno, “Shape classification using complex network and Multi-scale Fractal Dimension,” Pattern Recognition Letters, vol. 31, no. 1, pp. 44–51, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. E. Bax, “Validation of k-nearest neighbor classifiers,” IEEE Transactions on Information Theory, vol. 58, no. 5, pp. 3225–3234, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. F. Li, C. Wu, K. Wu, and J. Xu, “An improved back propagation neural network model and its application,” Journal of Computers, vol. 9, no. 8, pp. 1858–1862, 2014. View at Publisher · View at Google Scholar
  29. 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
  30. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Rolling element bearing fault diagnosis using wavelet transform,” Neurocomputing, vol. 74, no. 10, pp. 1638–1645, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. N. G. Nikolaou and I. A. Antoniadis, “Rolling element bearing fault diagnosis using wavelet packets,” NDT & E International, vol. 35, no. 3, pp. 197–205, 2002. View at Publisher · View at Google Scholar · View at Scopus
  32. Z. Shen, X. Chen, X. Zhang, and Z. He, “A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM,” Measurement, vol. 45, no. 1, pp. 30–40, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. 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, pp. 269–277, 2006. View at Google Scholar