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Computational Intelligence and Neuroscience
Volume 2012, Article ID 582453, 12 pages
http://dx.doi.org/10.1155/2012/582453
Research Article

Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

1Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal University, Karnataka, Manipal 576104, India
2Department of Mechanical Engineering, NMAM Institute of Technology, Karnataka, Nitte 574104, India
3Department of Mechanical Engineering, Vidya Vikas Institute of Engineering and Technology, Karnataka, Mysore 570028, India
4COMADEM International, Birmingham B29 6DA, UK

Received 31 March 2012; Revised 27 September 2012; Accepted 11 October 2012

Academic Editor: Karim Oweiss

Copyright © 2012 Vijay G. S. 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. Tandon and A. Choudhury, “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
  2. M. S. Patil, J. Mathew, and P. K. R. Kumar, “Bearing signature analysis as a medium for fault detection: a review,” Journal of Tribology, vol. 130, no. 1, Article ID 014001, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Huaigang, W. Zhibin, and Z. Ying, “Analysis of signal de-noising method based on an improved wavelet thresholding,” in Proceedings of the 9th International Conference on Electronic Measurement and Instruments (ICEMI '09), pp. 1987–1990, IEEE, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. H. T. Fang and D. S. Huang, “Wavelet de-noising by means of trimmed thresholding,” in Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA '04), pp. 1621–1624, Hangzhou, China, June 2004. View at Scopus
  5. Y. Lin and J. Cai, “A new threshold function for signal denoising based on wavelet transform,” in Proceedings of the International Conference on Measuring Technology and Mechatronics Automation (ICMTMA '10), pp. 200–203, IEEE. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Zhang, J. Wu, and Z. Cui, “Application of wavelet thresholding de-noising in DSA,” in Proceedings of the International Symposium on Information Science and Engineering (ISISE '08), pp. 130–134, IEEE, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Cai-lian, S. Ji-xiang, and K. Yao-hong, “Adaptive image denoising by a new thresholding function,” in Proceedings of the 6th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–5, IEEE, September 2010.
  8. Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199–221, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Michel, Y. Misiti, G. Oppenheim, and J. M. Poggi, “Wavelet Toolbox 4 User’s Guide,” 2010.
  10. X. Wang, Y. Zi, and Z. He, “Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis,” Mechanical Systems and Signal Processing, vol. 25, no. 1, pp. 285–304, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. 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
  13. J. Zarei, “Induction motors bearing fault detection using pattern recognition techniques,” Expert Systems with Applications, vol. 39, no. 1, pp. 68–73, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Fault diagnosis of ball bearings using machine learning methods,” Expert Systems with Applications, vol. 38, no. 3, pp. 1876–1886, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999. View at Google Scholar · View at Scopus
  16. Y. Yang, D. Yu, and J. Cheng, “A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,” Measurement, vol. 40, no. 9-10, pp. 943–950, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Sugumaran, V. Muralidharan, and K. I. Ramachandran, “Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 930–942, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Sreejith, A. K. Verma, and A. Srividya, “Fault diagnosis of rolling element bearing using time-domain features and neural networks,” in Proceedings of the IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems (ICIIS '08), vol. 409, pp. 1–6, Kharagpur, India, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. 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
  20. 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
  21. M. J. Fuente, D. Garcia-Alvarez, G. I. Sainz-Palmero, and T. Villegas, “Fault detection and identification method based on multivariate statistical techniques,” in Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation (ETFA '09), esp, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. L. H. Chiang, M. E. Kotanchek, and A. K. Kordon, “Fault diagnosis based on Fisher discriminant analysis and support vector machines,” Computers and Chemical Engineering, vol. 28, no. 8, pp. 1389–1401, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. X. C. Tang and Y. Li, “Monitoringand fault diagnosis using fisher discrimnant analysis,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), pp. 1100–1105, Hong Kong, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. L. B. Jack and A. K. Nandi, “Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms,” Mechanical Systems and Signal Processing, vol. 16, no. 2-3, pp. 373–390, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection,” Engineering Applications of Artificial Intelligence, vol. 16, no. 7-8, pp. 657–665, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Saxena and A. Saad, “Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 441–454, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, “LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines,” 2002, http://www.esat.kuleuven.ac.be/sista/lssvmlab.