Table of Contents Author Guidelines Submit a Manuscript
International Journal of Rotating Machinery
Volume 2017, Article ID 9602650, 10 pages
https://doi.org/10.1155/2017/9602650
Research Article

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

1Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, China
2Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China

Correspondence should be addressed to Kai Chen; nc.ude.tuhw@nehciak

Received 30 December 2016; Revised 14 March 2017; Accepted 29 March 2017; Published 11 June 2017

Academic Editor: Tonghai Wu

Copyright © 2017 Kai Chen 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. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks,” Journal of Marine Science and Application, vol. 10, no. 1, pp. 17–24, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Li, X. Yan, Z. Tian, C. Yuan, Z. Peng, and L. Li, “Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis,” Measurement Journal of the International Measurement Confederation, vol. 46, no. 1, pp. 259–271, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Chen, Z. Zhang, C. Sun, B. Li, Y. Zi, and Z. He, “Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors,” Mechanical Systems and Signal Processing, vol. 33, pp. 275–298, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. Ş. Ulus and S. Erkaya, “An experimental study on gear diagnosis by using acoustic emission technique,” International Journal of Acoustics and Vibrations, vol. 21, no. 1, pp. 103–111, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Li, Y. Jiang, C. Hu, and Z. Peng, “Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: a review,” Measurement, vol. 90, pp. 4–19, 2016. View at Publisher · View at Google Scholar
  6. J. Cheng, Y. Yang, and D. Yu, “The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis,” Mechanical Systems & Signal Processing, vol. 24, no. 2, pp. 508–521, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Li and Zheng, “Gear fault diagnosis based on order cepstrum analysis,” journal of Vibration & Shock, vol. 25, no. 5, pp. 65–68, 2006. View at Google Scholar · View at Scopus
  8. L. Nacib, K.-M. Pekpe, and S. Sakhara, “Detecting gear tooth cracks using cepstral analysis in gearbox of helicopters,” International Journal of Advances in Engineering & Technology, 2013. View at Google Scholar
  9. A. Djebala, N. Ouelaa, C. Benchaabane, and D. F. Laefer, “Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects,” Meccanica, vol. 47, no. 7, pp. 1601–1612, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M.-E. Morsy and G. Achtenova, “Value of autocorrelation analysis in vehicle gearbox fault diagnosis,” International Journal of Vehicle Noise & Vibration, vol. 11, no. 2, pp. 181–193, 2015. View at Google Scholar
  11. Y. Li, Z. Han, J. Gao, and S. Ning, “Application of EMD in gear fault diagnosis based on autocorrelation analysis,” Journal of Chinese Agricultural Mechanization, 2015. View at Google Scholar
  12. J. Cheng, D. Yu, and Y. Yang, “Fault diagnosis for rotor system based on EMD and fractal dimension,” China Mechanical Engineering, 2005. View at Google Scholar
  13. N. E. Huang, M. C. Wu, S. R. Long et al., “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” The Royal Society of London. Proceedings. Series A. Mathematical, Physical and Engineering Sciences, vol. 459, no. 2037, pp. 2317–2345, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” in Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4144–4147, Prague, Czech Republic, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems & Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Leng, S. Jing, and Z. Wu, “Fault diagnosis for gearbox based on RBF neural network,” Journal of Mechanical Strength, 2010. View at Google Scholar
  17. P. Jayaswal, S.-N. Verma, and A.-K. Wadhwani, “Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis,” Journal of Vibration & Control, vol. 17, no. 8, pp. 1131–1148, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. Y. Liu, S. Zhou, and Q. Chen, “Discriminative deep belief networks for visual data classification,” Pattern Recognition, vol. 44, no. 10-11, pp. 2287–2296, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Hatakeyama, H. Kataoka, Y. Okuhara, and S. Yoshida, “Decoding analysis for fMRI based on Deep Brief Network,” in 2014 World Automation Congress, WAC 2014, pp. 268–272, usa, August 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Kim, J. Nam, and I. Gurevych, “Learning semantics with deep belief network for Cross-Language information retrieval,” in International Conference on Computational Linguistics, pp. 579–588. View at Google Scholar
  22. P. Tamilselvan and P. Wang, “Failure diagnosis using deep belief learning based health state classification,” Reliability Engineering & System Safety, vol. 115, no. 7, pp. 124–135, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. F.-Y. Wang, J. J. Zhang, X. Zheng et al., “Where does AlphaGo go: From church-turing thesis to AlphaGo thesis and beyond,” IEEE/CAA Journal of Automatica Sinica, vol. 3, no. 2, pp. 113–120, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Lei, Z. He, and Y. Zi, “Application of the EEMD method to rotor fault diagnosis of rotating machinery,” Mechanical Systems & Signal Processing, vol. 23, no. 4, pp. 1327–1338, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Wang, C. Liu, F. Bi, X. Bi, and K. Shao, “Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension,” Mechanical Systems & Signal Processing, vol. 41, no. 1, pp. 581–597, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. G.-E. Hinton and T.-J. Sejnowski, “Learning and relearning in Boltzmann machines,” pp. 282–317, 1986. View at Google Scholar
  27. R. Salakhutdinov, A. Mnih, and G. Hinton, “Restricted Boltzmann machines for collaborative filtering,” in Proceedings of the 24th International Conference on Machine learning (ICML '07), vol. 227, pp. 791–798, Corvallis, Oregon, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Fischer and C. Igel, An Introduction to Restricted Boltzmann Machines, Springer Berlin Heidelberg, 2012.
  29. G. Hinton, Deep Belief Nets, Springer US, 2011.