Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 873905, 11 pages
http://dx.doi.org/10.1155/2015/873905
Research Article

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

1School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
3Engineering Workshop, Xi’an Jiaotong University, Xi’an 710049, China
4State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Received 19 September 2014; Revised 27 December 2014; Accepted 4 January 2015

Academic Editor: Saeed Balochian

Copyright © 2015 Lin Liang 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. Q. Hu, Z. J. He, Z. S. Zhang, and Y. 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
  2. Z. J. He, J. Y. Zhao, Y. B. He, and Q. F. Meng, “Wavelet transform and multiresolution signal decomposition for machinery monitoring and diagnosis,” in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '96), pp. 724–727, Shanghai, China, December 1996. View at Scopus
  3. Z. X. Li, X. P. Yan, C. Q. Yuan, J. B. Zhao, and Z. X. Peng, “A new method of nonlinear feature extraction for multi-fault diagnosis of rotor systems,” Noise and Vibration Worldwide, vol. 41, no. 10, pp. 29–37, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Xiong, S. X. Yang, and C. B. Gan, “A new procedure for extracting fault feature of multi-frequency signal from rotating machinery,” Mechanical Systems and Signal Processing, vol. 32, pp. 306–319, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. L. S. Qu and G. H. Xu, “One decade of holospectral technique: review and prospect,” in Proceedings of the ASME Design Engineering Technical Conferences (ADETC '99), pp. 12–15, Las Vegas, Nev, USA, 1999.
  6. B. C. Wang, Z. H. Ren, and B. C. Wen, “Fault diagnoses method of rotating machines based on nonlinear multi-parameters,” Journal of Mechanical Engineering, vol. 48, no. 5, pp. 63–69, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, no. 5500, pp. 2319–2323, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, no. 5500, pp. 2323–2326, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Y. Zhang and H. Y. Zha, “Principal manifolds and nonlinear dimensionality reduction via tangent space alignment,” Journal of Shanghai University, vol. 8, no. 4, pp. 406–424, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. J. Wang and Q. B. He, “Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery,” Journal of Sound and Vibration, vol. 333, no. 11, pp. 2450–2464, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. Q. B. He, “Vibration signal classification by wavelet packet energy flow manifold learning,” Journal of Sound and Vibration, vol. 332, no. 7, pp. 1881–1894, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. B. W. Li and Y. Zhang, “Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis,” Mechanical Systems and Signal Processing, vol. 25, no. 8, pp. 3125–3134, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. J. H. Yang, J. W. Xu, D. B. Yang, and M. Li, “Noise reduction method for nonlinear time series based on principal manifold learning and its application to fault diagnosis,” Chinese Journal of Mechanical Engineering, vol. 42, no. 8, pp. 154–158, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Li, J. W. Xu, J. H. Yang, D. B. Yang, and D. D. Wang, “Multiple manifolds analysis and its application to fault diagnosis,” Mechanical Systems and Signal Processing, vol. 23, no. 8, pp. 2500–2509, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. Q. S. Jiang, M. P. Jia, J. Z. Hu, and F. Y. Xu, “Method of fault pattern recognition based on laplacian eigenmaps,” Journal of System Simulation, vol. 20, no. 20, pp. 5710–5713, 2008. View at Google Scholar · View at Scopus
  16. S. K. Ng, T. Krishnan, and G. J. McLachlan, “The EM algorithm,” in Handbook of Computational Statistics, part 2, pp. 139–172, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  17. L. Y. Cao, “Practical method for determining the minimum embedding dimension of a scalar time series,” Physica D: Nonlinear Phenomena, vol. 110, no. 1-2, pp. 43–50, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Chelidze and M. Liu, “Multidimensional damage identification based on phase space warping: an experimental study,” Nonlinear Dynamics, vol. 46, no. 1-2, pp. 61–72, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980, vol. 898 of Lecture Notes in Mathematics, pp. 366–381, Springer, Berlin, Germany, 1981. View at Publisher · View at Google Scholar · View at MathSciNet
  20. M. Ragulskis and K. Lukoseviciute, “Non-uniform attractor embedding for time series forecasting by fuzzy inference systems,” Neurocomputing, vol. 72, no. 10–12, pp. 2618–2626, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. B. Zhan, J. P. Yin, X. W. Liu, and G. M. Zhang, “Adaptive neighborhood selection based on local linearity for manifold learning,” Journal of Computer Research and Development, vol. 48, no. 2, pp. 576–583, 2011. View at Google Scholar