Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 101757, 6 pages
http://dx.doi.org/10.1155/2015/101757
Research Article

Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

1HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China
2College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Received 8 April 2015; Revised 19 June 2015; Accepted 28 June 2015

Academic Editor: Jean-Charles Beugnot

Copyright © 2015 Jianfeng Zhang 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. X. Lin, Y.-X. Li, Y.-Q. Ma, and G. Wu, “Dynamic characteristics analysis on novel motor actuator of high voltage circuit breaker,” Electric Machines and Control, vol. 13, no. 2, pp. 216–226, 2009. View at Google Scholar · View at Scopus
  2. A. A. Polycarpou, A. Soom, V. Swarnakar et al., “Event timing and shape analysis of vibration bursts from power circuit breakers,” IEEE Transactions on Power Delivery, vol. 11, no. 2, pp. 848–857, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Huang, X.-G. Hu, Y.-N. Gong, and F. Yang, “Machinery fault diagnosis expert system for high voltage circuit breaker,” Electric Machines and Control, vol. 15, no. 10, pp. 43–49, 2011. View at Google Scholar · View at Scopus
  4. J. Li, “New signal process scheme based on wavelet for online monitoring of high voltage circuit breaker,” Journal of Beijing Institute of Machinery, vol. 22, no. 1, pp. 19–22, 2007. View at Google Scholar
  5. X. G. Hu, J. Z. Wang, Y. C. Ji et al., “The application of the wavelet analysis of analytic signals in mechanical fault diagnosis of circuit breakers,” in Proceedings of the IEEE Power Engineering Society General Meeting, vol. 4, pp. 2235–2240, IEEE/PES, Toronto, Canada, July 2003.
  6. Q. Ma, M. Z. Rong, and S. L. Jia, “Study of switching synchronization of high voltage breakers based on the wavelet packets extraction algorithm and short time analysis method,” Proceedings of the Chinese Society for Electrical Engineering, vol. 25, no. 13, pp. 149–154, 2005. View at Google Scholar
  7. C. Lu and X. G. Hu, “A new method of fault diagnosis for high-voltage circuit-breakers based on Hilbert-Huang transform,” in Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA '07), pp. 2697–2701, IEEE, Harbin, China, May 2007. View at Publisher · View at Google Scholar
  8. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Liang, Z. Wang, A. Maier, and N. K. Logothetis, “Single-trial classification of bistable perception by integrating empirical mode decomposition, clustering, and support vector machine,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 592742, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Liang and Z. Lin, “Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transform,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 7, pp. 681–688, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Wang, A. Maier, D. A. Leopold, N. K. Logothetis, and H. Liang, “Single-trial evoked potential estimation using wavelets,” Computers in Biology and Medicine, vol. 37, no. 4, pp. 463–473, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Liang, S. L. Bressler, E. A. Buffalo, R. Desimone, and P. Fries, “Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention,” Biological Cybernetics, vol. 92, no. 6, pp. 380–392, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. H. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Chen, Y. Zi, Z. He, and W. Cheng, “Research and application of ensemble empirical mode decomposition principle and 1.5 dimension spectrum method,” Journal of Xi'an Jiaotong University, vol. 43, no. 5, pp. 94–98, 2009. View at Google Scholar · View at Scopus
  15. M. Hu and H. Liang, “Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis,” Cognitive Neurodynamics, vol. 5, no. 3, pp. 277–284, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Rakoczy, M. Kordas, G. Story, and M. Konopacki, “The characterization of the residence time distribution in a magnetic mixer by means of the information entropy,” Chemical Engineering Science, vol. 105, no. 2, pp. 191–197, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Su, J. Hu, and Z. Wen, “Structure analysis for concrete-faced rockfill dams based on information entropy theory and finite element method,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 36, no. 8, pp. 1041–1055, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. L. Li, R. P. Shao, and J. M. Cao, “A new and effective method of gear fault diagnosis using wavelet packet transform combined with support vector machine,” Journal of Northwestern Polytechnical University, vol. 28, no. 4, pp. 530–535, 2010. View at Google Scholar · View at Scopus
  19. H. Liang and Z. Lin, “Detection of delayed gastric emptying from electrogastrograms with support vector machine,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 5, pp. 601–604, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Y. Meng and X. Y. Liu, “A new SVM multi-class classification based on binary tree,” Computer Applications, vol. 25, no. 11, pp. 2653–2657, 2005. View at Google Scholar
  21. Z. G. Yan and P. J. Du, “Construction methods for H-SVMs,” Journal of Southeast University, vol. 39, no. 1, pp. 204–209, 2009. View at Google Scholar · View at Scopus