Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2017, Article ID 5963239, 14 pages
https://doi.org/10.1155/2017/5963239
Research Article

Casing Vibration Fault Diagnosis Based on Variational Mode Decomposition, Local Linear Embedding, and Support Vector Machine

State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Yizhou Yang; nc.ude.auhgnist.sliam@61zygnay

Received 10 October 2016; Revised 17 January 2017; Accepted 16 February 2017; Published 12 March 2017

Academic Editor: Dario Di Maio

Copyright © 2017 Yizhou Yang and Dongxiang Jiang. 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. Guo, “Study on the recognition of aero-engine blade-casing rubbing fault based on the casing vibration acceleration,” Measurement: Journal of the International Measurement Confederation, vol. 65, pp. 71–80, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. K. Peng, P. W. Tse, and F. L. Chu, “A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing,” Mechanical Systems & Signal Processing, vol. 19, no. 5, pp. 974–988, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. N. E. Huang, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Google Scholar
  5. J. S. Smith, “The local mean decomposition and its application to EEG perception data,” Journal of the Royal Society Interface, vol. 2, no. 5, pp. 443–454, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. M. G. Frei and I. Osorio, “Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 463, no. 2078, pp. 321–342, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. H. U. Jie, “Survey on feature dimension reduction for high-dimensional data,” Application Research of Computers, vol. 25, no. 9, pp. 2601–2606, 2008. View at Google Scholar
  9. I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 1986. View at Publisher · View at Google Scholar
  10. 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
  11. A. J. Hoffman and N. T. Van Der Merwe, “The application of neural networks to vibrational diagnostics for multiple fault conditions,” Computer Standards and Interfaces, vol. 24, no. 2, pp. 139–149, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. V. N. Vapnik, The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, Springer, New York, NY, USA, 2nd edition, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  13. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining & Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. X. An and J. Yang, “Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy,” Transactions of the Institute of Measurement and Control, vol. 38, no. 3, pp. 282–292, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Liu, Z. Yu, M. Zeng, and Y. Zhang, “An improved LLE algorithm based on iterative shrinkage for machinery fault diagnosis,” Measurement: Journal of the International Measurement Confederation, vol. 77, pp. 246–256, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. J. A. K. Suykens, “Nonlinear modelling and support vector machines,” in Proceedings of the IEEE Instrumentation & Measurement Technology Conference, vol. 1, pp. 287–294, IEEE, Budapest, Hungary, May 2001.
  17. H. Prashad, M. Ghosh, and S. Biswas, “Diagnostic monitoring of rolling-element bearings by high-frequency resonance technique,” ASLE Transactions, vol. 28, no. 4, pp. 439–448, 1985. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Rilling, P. Flandrin, and P. Goncalves, “On empirical mode decomposition and its algorithms,” in Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03), Grado, Italy, June 2003.
  19. G. Chen, “Simulation of casing vibration resulting from blade-casing rubbing and its verifications,” Journal of Sound and Vibration, vol. 361, pp. 190–209, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Han and D. Jiang, “Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier,” Shock and Vibration, vol. 2016, Article ID 5132046, 11 pages, 2016. View at Publisher · View at Google Scholar
  21. E. Mayoraz and E. Alpaydin, “Support vector machines for multi-class classification,” in Engineering Applications of Bio-Inspired Artificial Neural Networks, vol. 1607 of Lecture Notes in Computer Science, pp. 833–842, Springer, Berlin, Germany, 1999. View at Publisher · View at Google Scholar
  22. A. J. Hoffman and N. T. Van Der Merwe, “The application of neural networks to vibrational diagnostics for multiple fault conditions,” Computer Standards & Interfaces, vol. 24, no. 2, pp. 139–149, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition, CRC Press, 2006.
  24. C. Jing and J. Hou, “SVM and PCA based fault classification approaches for complicated industrial process,” Neurocomputing, vol. 167, pp. 636–642, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. Z. Su, B. Tang, J. Ma, and L. Deng, “Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier,” Measurement: Journal of the International Measurement Confederation, vol. 48, no. 1, pp. 136–148, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. L. J. P. Van Der Maaten, E. O. Postma, and H. J. V. D. Herik, “Dimensionality reduction: a comparative review,” Journal of Machine Learning Research, vol. 10, no. 1, 2007. View at Google Scholar
  27. L. van der Maaten, Matlab Toolbox for Dimensionality Reduction, 2007.