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Shock and Vibration
Volume 2015, Article ID 106945, 12 pages
http://dx.doi.org/10.1155/2015/106945
Research Article

Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Department of Applied Mechanics and Design, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310 Johor, Malaysia

Received 28 December 2014; Accepted 2 March 2015

Academic Editor: Mickaël Lallart

Copyright © 2015 Yasir Hassan Ali 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. C. Sreepradha, A. K. Kumari, A. E. Perumal, R. C. Panda, K. Harshabardhan, and M. Aribalagan, “Neural network model for condition monitoring of wear and film thickness in a gearbox,” Neural Computing and Applications, vol. 24, no. 7-8, pp. 1943–1952, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. 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
  3. Z. Peng and N. Kessissoglou, “An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis,” Wear, vol. 255, no. 7-12, pp. 1221–1232, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. A. K. Mahamad and T. Hiyama, “Fault classification based artificial intelligent methods of induction motor bearing,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 9, pp. 5477–5494, 2011. View at Google Scholar · View at Scopus
  5. C. K. Sung, H. M. Tai, and C. W. Chen, “Locating defects of a gear system by the technique of wavelet transform,” Mechanism and Machine Theory, vol. 35, no. 8, pp. 1169–1182, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Amarnath, C. Sujatha, and S. Swarnamani, “Experimental studies on the effects of reduction in gear tooth stiffness and lubricant film thickness in a spur geared system,” Tribology International, vol. 42, no. 2, pp. 340–352, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. R. I. Raja Hamzah and D. Mba, “Acoustic emission and specific film thickness for operating spur gears,” Journal of Tribology, vol. 129, no. 4, pp. 860–867, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. B.-R. Höhn and K. Michaelis, “Influence of oil temperature on gear failures,” Tribology International, vol. 37, no. 2, pp. 103–109, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. R. I. R. Hamzah and D. Mba, “The influence of operating condition on acoustic emission (AE) generation during meshing of helical and spur gear,” Tribology International, vol. 42, no. 1, pp. 3–14, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. R. R. Hamzah, K. R. Al-Balushi, and D. Mba, “Observations of acoustic emission under conditions of varying specific film thickness for meshing spur and helical gears,” Journal of Tribology, vol. 130, no. 2, pp. 1–12, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Hamel, A. Addali, and D. Mba, “Monitoring oil film regimes with acoustic emission,” Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, vol. 228, no. 2, pp. 223–231, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Samanta and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features,” Mechanical Systems and Signal Processing, vol. 17, no. 2, pp. 317–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Samanta, “Gear fault detection using artificial neural networks and support vector machines with genetic algorithms,” Mechanical Systems and Signal Processing, vol. 18, no. 3, pp. 625–644, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Rajakarunakaran, P. Venkumar, D. Devaraj, and K. S. P. Rao, “Artificial neural network approach for fault detection in rotary system,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 740–748, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Lei, Z. He, Y. Zi, and Q. Hu, “Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2280–2294, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. J. M. F. Salido and S. Murakami, “A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery,” Applied Soft Computing Journal, vol. 4, no. 4, pp. 413–422, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. Q. Hu, Z. He, Z. Zhang, and Y. Zi, “Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 688–705, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. 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
  19. Y. Lei, Z. He, Y. Zi, and X. Chen, “New clustering algorithm-based fault diagnosis using compensation distance evaluation technique,” Mechanical Systems and Signal Processing, vol. 22, no. 2, pp. 419–435, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Hamel, A. Addali, and D. Mba, “Investigation of the influence of oil film thickness on helical gear defect detection using Acoustic Emission,” Applied Acoustics, vol. 79, pp. 42–46, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. H. Ali, R. Abd Rahman, and R. I. R. Hamzah, “Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review,” Jurnal Teknologi, vol. 69, no. 2, 2014. View at Publisher · View at Google Scholar
  22. Y. H. Ali, M. R. Omar, R. A. Abd Rahman, and R. I. R. Hamzah, “Acoustic emission technique in condition monitoring and fault diagnosis of gears and bearings,” International Journal of Academic Research Part A, vol. 6, no. 5, article 6, 2014. View at Google Scholar
  23. C. K. Tan and D. Mba, “Identification of the acoustic emission source during a comparative study on diagnosis of a spur gearbox,” Tribology International, vol. 38, no. 5, pp. 469–480, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. K. P. Ferentinos, “Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms,” Neural Networks, vol. 18, no. 7, pp. 934–950, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Rafiee, F. Arvani, A. Harifi, and M. H. Sadeghi, “Intelligent condition monitoring of a gearbox using artificial neural network,” Mechanical Systems and Signal Processing, vol. 21, no. 4, pp. 1746–1754, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. A. K. Mahamad, S. Saon, and T. Hiyama, “Predicting remaining useful life of rotating machinery based artificial neural network,” Computers and Mathematics with Applications, vol. 60, no. 4, pp. 1078–1087, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. K. G. Sheela and S. N. Deepa, “Review on methods to fix number of hidden neurons in neural networks,” Mathematical Problems in Engineering, vol. 2013, Article ID 425740, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus