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Shock and Vibration
Volume 2016 (2016), Article ID 6127479, 12 pages
http://dx.doi.org/10.1155/2016/6127479
Research Article

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

1School of Reliability and Systems Engineering, Beihang University, Beijing, China
2Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing, China

Received 26 April 2016; Accepted 20 July 2016

Academic Editor: Fiorenzo A. Fazzolari

Copyright © 2016 Hongmei Liu 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. K. Shibata, A. Takahashi, and T. Shirai, “Fault diagnosis of rotating machinery through visualisation of sound signals,” Mechanical Systems and Signal Processing, vol. 14, no. 2, pp. 229–241, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Lin, “Feature extraction of machine sound using wavelet and its application in fault diagnosis,” NDT & E International, vol. 34, no. 1, pp. 25–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Yu, J. Cheng, and Y. Yang, “Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings,” Mechanical Systems and Signal Processing, vol. 19, no. 2, pp. 259–270, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Y. Liu, W. H. Zhang, J. G. Han, and G. F. Wang, “A new wind turbine fault diagnosis method based on the local mean decomposition,” Renewable Energy, vol. 48, pp. 411–415, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. 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, pp. 273–288, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 21, no. 3, pp. 1300–1317, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. S. W. Choi, C. Lee, J.-M. Lee, J. H. Park, and I.-B. Lee, “Fault detection and identification of nonlinear processes based on kernel PCA,” Chemometrics and Intelligent Laboratory Systems, vol. 75, no. 1, pp. 55–67, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “The fault diagnosis approach for gears using multidimensional features and intelligent classifier,” Noise & Vibration Worldwide, vol. 41, no. 10, pp. 76–86, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Wei, Z. Weijia, and L. Bin, “Fault diagnosis approach based on fractal dimension LLE and Fisher discriminant,” Chinese Journal of Scientific Instrument, vol. 31, no. 2, pp. 325–333, 2010. View at Google Scholar
  10. G. B. Wang, X. Q. Zhao, and Y. H. He, “Fault diagnosis method based on supervised incremental local tangent space alignment and SVM,” Applied Mechanics and Materials, vol. 34-35, pp. 1233–1237, 2010. View at Publisher · View at Google Scholar
  11. L. Shuang and L. Meng, “Bearing fault diagnosis based on PCA and SVM,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '07), pp. 3503–3507, IEEE, Harbin, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Bagheri, H. Ahmadi, and R. Labbafi, “Application of data mining and feature extraction on intelligent fault diagnosis by artificial neural network and k-nearest neighbor,” in Proceedings of the 19th International Conference on Electrical Machines (ICEM '10), pp. 1–7, IEEE, Rome, Italy, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Chen and C. Mo, “A method for intelligent fault diagnosis of rotating machinery,” Digital Signal Processing, vol. 14, no. 3, pp. 203–217, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. M. K. Kıymık, I. Güler, A. Dizibüyük, and M. Akin, “Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application,” Computers in Biology and Medicine, vol. 35, no. 7, pp. 603–616, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Jurado and J. R. Saenz, “Comparison between discrete STFT and wavelets for the analysis of power quality events,” Electric Power Systems Research, vol. 62, no. 3, pp. 183–190, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Deng and I. Kheirallah, “Dynamic formant tracking of noisy speech using temporal analysis on outputs from a nonlinear cochlear model,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 5, pp. 456–467, 1993. View at Publisher · View at Google Scholar · View at Scopus
  18. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Bengio and Y. LeCun, “Scaling learning algorithms towards AI,” Large-Scale Kernel Machines, vol. 34, no. 5, pp. 1–41, 2007. View at Google Scholar
  20. H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, “Exploring strategies for training deep neural networks,” The Journal of Machine Learning Research, vol. 10, pp. 1–40, 2009. View at Google Scholar · View at Scopus
  21. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103, ACM, Helsinki, Finland, July 2008. View at Scopus
  22. Unsupervised Feature Learning and Deep Learning (UFLDL), 2011, http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial.
  23. Y. L. Boureau, S. Chopra, and Y. Lecun, “A unified energy-based framework for unsupervised learning,” in Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 371–379, 2007.
  24. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 315–323, Ft. Lauderdale, Fla, USA, April 2011.
  25. N. K. Verma, V. K. Gupta, M. Sharma, and R. K. Sevakula, “Intelligent condition based monitoring of rotating machines using sparse auto-encoders,” in Proceedings of the IEEE Conference on Prognostics and Health Management (PHM '13), pp. 1–7, June 2013.
  26. K. Duan, S. S. Keerthi, W. Chu, S. K. Shevade, and A. N. Poo, “Multi-category classification by soft-max combination of binary classifiers,” in Multiple Classifier Systems, vol. 2709 of Lecture Notes in Computer Science, pp. 125–134, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar