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Shock and Vibration
Volume 2016, Article ID 1232893, 12 pages
http://dx.doi.org/10.1155/2016/1232893
Research Article

A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution

1State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
2School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
3CNR Changchun Railway Vehicle Co., Changchun, Jilin 130062, China

Received 28 April 2015; Accepted 31 August 2015

Academic Editor: Didier Rémond

Copyright © 2016 Qing Xiong 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. Z. Zhang, X. Shi, Q. Shi, and B. Tang, “Fault feature extraction of rolling element bearing based on improved EMD and spectral kurtosis,” Journal of Vibration, Measurement and Diagnosis, vol. 33, no. 3, pp. 478–482, 2013. View at Google Scholar · View at Scopus
  2. A. Bourdon, D. Rémond, S. Chesné, and H. André, “Reconstruction of the instantaneous angular speed variations caused by a spall defect on a rolling bearing outer ring correlated with the length of the defect,” in Advances in Condition Monitoring of Machinery in Non-Stationary Operations, Lecture Notes in Mechanical Engineering, pp. 335–345, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  3. J. B. 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, pp. 16–27, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Zhang, Y. Liang, J. Zhou, and Y. zang, “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM,” Measurement, vol. 69, pp. 164–179, 2015. View at Publisher · View at Google Scholar
  5. M. Kang, J. Kim, and J.-M. Kim, “Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm,” Information Sciences, vol. 294, pp. 423–438, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  6. T. W. Rauber, F. de Assis Boldt, and F. M. Varejão, “Heterogeneous feature models and feature selection applied to bearing fault diagnosis,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 637–646, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Sharma, M. Amarnath, and P. Kankar, “Feature extraction and fault severity classification in ball bearings,” Journal of Vibration and Control, 2014. View at Publisher · View at Google Scholar
  8. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Liu and M. Han, “A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings,” Mechanism and Machine Theory, vol. 75, pp. 67–78, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Li, Y. Jiang, and J. Xiang, “Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification,” The Scientific World Journal, vol. 2014, Article ID 145807, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. E. A. F. Ihlen and B. Vereijken, “Multifractal formalisms of human behavior,” Human Movement Science, vol. 32, no. 4, pp. 633–651, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. E. A. F. Ihlen, “The influence of power law distributions on long-range trial dependency of response times,” Journal of Mathematical Psychology, vol. 57, no. 5, pp. 215–224, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. W.-C. Yang, C.-H. Zhao, and B.-Z. Cheng, “Recognition of communication signals in noise with alpha stable distribution,” Journal of Applied Sciences, vol. 28, no. 2, pp. 111–114, 2010. View at Google Scholar · View at Scopus
  14. H. Liu, X. Wang, and C. Lu, “Rolling bearing fault diagnosis based on LCD–TEO and multifractal detrended fluctuation analysis,” Mechanical Systems and Signal Processing, vol. 60-61, pp. 273–288, 2015. View at Publisher · View at Google Scholar
  15. J. Lin and Q. Chen, “Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion,” Mechanical Systems and Signal Processing, vol. 38, no. 2, pp. 515–533, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Yu, C. Li, and J. Zhang, “A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution,” Mechanical Systems and Signal Processing, vol. 41, no. 1-2, pp. 155–175, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Yu and N. Shi, “Gear fault signal modeling and detection based on alpha stable distribution,” in Proceedings of the International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA '12), pp. 471–474, Sanya, China, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Saidi, J. Ben Ali, and F. Fnaiech, “Application of higher order spectral features and support vector machines for bearing faults classification,” ISA Transactions, vol. 54, pp. 193–206, 2015. View at Publisher · View at Google Scholar
  19. K. Zhu, X. Song, and D. Xue, “A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm,” Measurement, vol. 47, no. 1, pp. 669–675, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Wang, Y. Zheng, Z. Zhao, and J. Wang, “Bearing fault diagnosis based on statistical locally linear embedding,” Sensors, vol. 15, no. 7, pp. 16225–16247, 2015. View at Publisher · View at Google Scholar
  21. M. Yuwono, Y. Qin, J. Zhou, Y. Guo, B. G. Celler, and S. W. Su, “Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model,” Engineering Applications of Artificial Intelligence, 2015. View at Publisher · View at Google Scholar
  22. J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, “Multifractal detrended fluctuation analysis of nonstationary time series,” Physica A: Statistical Mechanics and Its Applications, vol. 316, no. 1–4, pp. 87–114, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Shang, Y. Lu, and S. Kamae, “Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis,” Chaos, Solitons and Fractals, vol. 36, no. 1, pp. 82–90, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Samorodnitsky and M. S. Taqqu, Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance, Chapman & Hall, Boca Raton, Fla, USA, 1994. View at MathSciNet
  25. M. Shao and C. L. Nikias, “Signal processing with fractional lower order moments: stable processes and their applications,” Proceedings of the IEEE, vol. 81, no. 7, pp. 986–1010, 1993. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear component analysis as a Kernel Eigenvalue Problem,” Neural Computation, vol. 10, no. 5, pp. 1299–1319, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Žvokelj, S. Zupan, and I. Prebil, “Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method,” Mechanical Systems and Signal Processing, vol. 25, no. 7, pp. 2631–2653, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. del Valle, G. K. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, and R. G. Harley, “Particle swarm optimization: basic concepts, variants and applications in power systems,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 2, pp. 171–195, 2008. View at Publisher · View at Google Scholar · View at Scopus
  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. Z. Yang, T. Peng, J. Li, and Y. Zhong, “Bayesian inference LSSVM based fault diagnosis method for rolling bearing,” Journal of Electronic Measurement and Instrument, vol. 24, no. 5, pp. 420–424, 2010. View at Google Scholar
  31. K. A. Loparo, Bearings Vibration Data Set, [EB/OL], Case Western Reserve University, 2008, http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
  32. I. A. Koutrouvelis, “An iterative procedure for the estimation of the parameters of stable laws,” Communications in Statistics. B. Simulation and Computation, vol. 10, no. 1, pp. 17–28, 1981. View at Publisher · View at Google Scholar · View at MathSciNet