Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 354658, 7 pages
http://dx.doi.org/10.1155/2015/354658
Research Article

ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

1School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China
2Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada T2N 1N4

Received 24 November 2014; Accepted 12 January 2015

Academic Editor: Yun-Bo Zhao

Copyright © 2015 Zhihuai Xiao 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. D.-L. Zhao, W. Ma, W.-K. Liang, and X.-Q. Luo, “Data fusion fault diagnosis and simulation of hydroelectric units vibration,” Proceedings of the Chinese Society of Electrical Engineering, vol. 25, no. 20, pp. 137–142, 2005 (Chinese). View at Google Scholar · View at Scopus
  2. D. Shen, F. Chu, and S. Chen, “Diagnosis and identification of vibration accident for hydro-generator unit,” Journal of Hydrodynamics, vol. 15, no. 1, pp. 129–133, 2000 (Chinese). View at Google Scholar
  3. X. Fu, G.-L. Liu, and J. Jiang, “Application of BP neural networks to condition monitoring and fault diagnosis system of hydro-generator units,” Engineering Journal of Wuhan University, vol. 35, no. 1, pp. 24–28, 2002 (Chinese). View at Google Scholar
  4. R. Jia, L. Bai, X. Luo, and F. Liu, “Expert system on fault diagnosis based on neural network for hydropower units,” Journal of Hydroelectric Engineering, vol. 23, no. 6, pp. 120–123, 2004. View at Google Scholar · View at Scopus
  5. X. Yang, J. Xie, and C. Sun, “Neural network method for vibration fault diagnosis of hydroelectric generating set,” Journal of Hydraulic Engineering, supplement 1, pp. 94–97, 1998 (Chinese). View at Google Scholar
  6. W. Peng, P. Guo, and X. Luo, “Research on vibration fault diagnosis of hydro-turbine generating unit based on LS-SVM and information fusion technology,” Journal of Hydroelectric Engineering, vol. 26, no. 6, pp. 137–142, 2007. View at Google Scholar · View at Scopus
  7. D. Zhang and L. Yu, “Exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays,” Neural Networks, vol. 35, pp. 103–111, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Peng and X. Luo, “Research on vibration fault diagnosis of hydro-turbine generating unit based on wavelet neural network,” Journal of Hydroelectric Engineering, vol. 26, no. 1, pp. 123–128, 2007 (Chinese). View at Google Scholar · View at Scopus
  9. Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 889–898, 1992. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Cheng and Y. Bai, “A novel approach to fuzzy wavelet neural network modeling and optimization,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 671–678, 2015. View at Publisher · View at Google Scholar
  11. Z. Zainuddin and O. Pauline, “Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 4866–4874, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Cao, Z. Lin, and G.-B. Huang, “Composite function wavelet neural networks with extreme learning machine,” Neurocomputing, vol. 73, no. 7-9, pp. 1405–1416, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. T.-S. Wang and L.-P. Zhang, “Application of immune wavelet network model to fault diagnosis of hydro-turbine generating units,” Journal of Hydraulic Engineering, vol. 40, no. 6, pp. 762–767, 2009 (Chinese). View at Google Scholar · View at Scopus
  14. L. Zuo, L.-G. Hou, D.-M. Gao, X.-H. Peng, and W.-C. Wu, “Fault diagnosis of analog circuit based on PSO-WNN,” Journal of Beijing University of Technology, vol. 36, no. 3, pp. 306–309, 2010 (Chinese). View at Google Scholar · View at Scopus
  15. D. Feng, Application Research of Ant Colony Algorithm and Wavelet Network in Complexity Science, Tianjin University, Tianjin, China, 2008, (Chinese).
  16. L. Liu, Y. Li, and W. Wang, “Fault diagnosis study on the vibration of hydropower units based on GA neural network and evidence theory fusion,” Journal of Hydroelectric Engineering, vol. 27, no. 5, pp. 163–167, 2008. View at Google Scholar · View at Scopus