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Shock and Vibration
Volume 2015, Article ID 847802, 8 pages
http://dx.doi.org/10.1155/2015/847802
Research Article

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

1College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415003, China
2College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
3Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 70000, Vietnam
4Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh 70000, Vietnam
5Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh 70000, Vietnam

Received 5 December 2014; Accepted 19 March 2015

Academic Editor: Anindya Ghoshal

Copyright © 2015 Songrong Luo 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. 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
  2. B. Assaad, M. Eltabach, and J. Antoni, “Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes,” Mechanical Systems and Signal Processing, vol. 42, no. 1-2, pp. 351–367, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Li, Z. Ma, Y. Liu, W. Teng, and R. Jiang, “Crack fault detection for a gearbox using discrete wavelet transform and an adaptive resonance theory neural network,” Journal of Mechanical Engineering, vol. 61, no. 1, pp. 63–73, 2015. View at Publisher · View at Google Scholar
  4. B. Muruganatham, M. A. Sanjith, B. Krishnakumar, and S. A. V. Satya Murty, “Roller element bearing fault diagnosis using singular spectrum analysis,” Mechanical Systems and Signal Processing, vol. 35, no. 1-2, pp. 150–166, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Kilundu, X. Chiementin, and P. Dehombreux, “Singular spectrum analysis for bearing defect detection,” Journal of Vibration and Acoustics, Transactions of the ASME, vol. 133, no. 5, Article ID 051007, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. 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
  7. 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 A, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  8. J. S. Smith, “The local mean decomposition and its application to EEG perception data,” Journal of the Royal Society Interface, vol. 2, no. 5, pp. 443–454, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Rilling and P. Flandrin, “One or two frequencies? The empirical mode decomposition answers,” IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 85–95, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  10. 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: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Cheng, D. Yu, and Y. Yang, “Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform,” Mechanical Systems and Signal Processing, vol. 21, no. 3, pp. 1197–1211, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. J.-S. Cheng, J.-D. Zheng, and Y. Yang, “A nonstationary signal analysis approach—the local characteristic-scale decomposition method,” Journal of Vibration Engineering, vol. 25, no. 2, pp. 215–220, 2012. View at Google Scholar · View at Scopus
  13. J. Zheng, J. Cheng, and Y. Yang, “A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy,” Mechanism and Machine Theory, vol. 70, pp. 441–453, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Lei and M. J. Zuo, “Gear crack level identification based on weighted K nearest neighbor classification algorithm,” Mechanical Systems and Signal Processing, vol. 23, no. 5, pp. 1535–1547, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. C.-C. Wang, Y. Kang, P.-C. Shen, Y.-P. Chang, and Y.-L. Chung, “Applications of fault diagnosis in rotating machinery by using time series analysis with neural network,” Expert Systems with Applications, vol. 37, no. 2, pp. 1696–1702, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Zhang and J. Zhou, “Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines,” Mechanical Systems and Signal Processing, vol. 41, no. 1-2, pp. 127–140, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. H. Xu and G. Chen, “An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO,” Mechanical Systems and Signal Processing, vol. 35, no. 1-2, pp. 167–175, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998. View at MathSciNet
  20. S. Abe, Advances in Pattern Recognition, Springer, London, UK, 2005.
  21. F. Friedrichs and C. Igel, “Evolutionary tuning of multiple SVM parameters,” Neurocomputing, vol. 64, no. 1-4, pp. 107–117, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. X. L. Zhang, X. F. Chen, and Z. J. He, “An ACO-based algorithm for parameter optimization of support vector machines,” Expert Systems with Applications, vol. 37, no. 9, pp. 6618–6628, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Y. S. Lam and V. O. K. Li, “Chemical-reaction-inspired metaheuristic for optimization,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 381–399, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Y. S. Lam and V. O. K. Li, “Chemical reaction optimization: a tutorial,” Memetic Computing, vol. 4, no. 1, pp. 3–17, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. 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
  26. H. L. Ao, J. Cheng, K. Li, and T. K. Truong, “A roller bearing fault diagnosis method based on LCD energy entropy and ACROA-SVM,” Shock and Vibration, vol. 2014, Article ID 825825, 12 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Ao, J. Cheng, J. Zheng, and T. K. Truong, “Roller bearing fault diagnosis method based on chemical reaction optimization and support vector machine,” Journal of Computing in Civil Engineering, Article ID 04014077, 2014. View at Publisher · View at Google Scholar