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Shock and Vibration
Volume 2016 (2016), Article ID 3989743, 12 pages
http://dx.doi.org/10.1155/2016/3989743
Research Article

Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

1Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

Received 17 November 2015; Accepted 1 March 2016

Academic Editor: Abdollah Shafieezadeh

Copyright © 2016 Lin-sheng Huo 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. Y. J. Yan, L. Cheng, Z. Y. Wu, and L. H. Yam, “Development in vibration-based structural damage detection technique,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2198–2211, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Yang, L. Wang, H. Wang, Y. Ding, and X. Dang, “Damage detection in composite structures using vibration response under stochastic excitation,” Journal of Sound and Vibration, vol. 325, no. 4-5, pp. 755–768, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. J.-T. Kim, Y.-S. Ryu, H.-M. Cho, and N. Stubbs, “Damage identification in beam-type structures: frequency-based method vs mode-shape-based method,” Engineering Structures, vol. 25, no. 1, pp. 57–67, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Worden, G. Manson, and N. R. J. Fieller, “Damage detection using outlier analysis,” Journal of Sound and Vibration, vol. 229, no. 3, pp. 647–667, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Zang, M. I. Friswell, and M. Imregun, “Structural damage detection using independent component analysis,” Structural Health Monitoring, vol. 3, no. 1, pp. 69–83, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Hera and Z. Hou, “Application of wavelet approach for ASCE structural health monitoring benchmark studies,” Journal of Engineering Mechanics, vol. 130, no. 1, pp. 96–104, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Cantero and B. Basu, “Railway infrastructure damage detection using wavelet transformed acceleration response of traversing vehicle,” Structural Control and Health Monitoring, vol. 22, no. 1, pp. 62–70, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. J.-P. Tang, D.-J. Chiou, C.-W. Chen et al., “RETRACTED: a case study of damage detection in benchmark buildings using a Hilbert-Huang Transform-based method,” Journal of Vibration and Control, vol. 17, no. 4, pp. 623–636, 2011. View at Publisher · View at Google Scholar
  9. J. N. Yang, Y. Lei, S. Lin, and N. Huang, “Hilbert-Huang based approach for structural damage detection,” Journal of Engineering Mechanics, vol. 130, no. 1, pp. 85–95, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. Yang, Z. Yu, and H. Sun, “On the cross correlation function amplitude vector and its application to structural damage detection,” Mechanical Systems and Signal Processing, vol. 21, no. 7, pp. 2918–2932, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Wang, Z. Yang, and T. P. Waters, “Structural damage detection using cross correlation functions of vibration response,” Journal of Sound and Vibration, vol. 329, no. 24, pp. 5070–5086, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Wang and Z. Yang, “Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses,” Journal of Sound and Vibration, vol. 332, no. 4, pp. 645–653, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. W. T. Yeung and J. W. Smith, “Damage detection in bridges using neural networks for pattern recognition of vibration signatures,” Engineering Structures, vol. 27, no. 5, pp. 685–698, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Qian and A. Mita, “Acceleration-based damage indicators for building structures using neural network emulators,” Structural Control and Health Monitoring, vol. 15, no. 6, pp. 901–920, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. H.-X. He and W.-M. Yan, “Structural damage detection with wavelet support vector machine: introduction and applications,” Structural Control and Health Monitoring, vol. 14, no. 1, pp. 162–176, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Widodo and B.-S. Yang, “Wavelet support vector machine for induction machine fault diagnosis based on transient current signal,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 307–316, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Q. Ni, X. G. Hua, K. Q. Fan, and J. M. Ko, “Correlating modal properties with temperature using long-term monitoring data and support vector machine technique,” Engineering Structures, vol. 27, no. 12, pp. 1762–1773, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Worden and A. J. Lane, “Damage identification using support vector machines,” Smart Materials and Structures, vol. 10, no. 3, pp. 540–547, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. C. K. Oh and H. Sohn, “Damage diagnosis under environmental and operational variations using unsupervised support vector machine,” Journal of Sound and Vibration, vol. 325, no. 1-2, pp. 224–239, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Song, L. Zhong, and B. Han, “Structural damage detection by integrating independent component analysis and support vector machine,” International Journal of Systems Science, vol. 37, no. 13, pp. 961–967, 2006. View at Publisher · View at Google Scholar
  22. Y. Yang, D. Yu, and J. Cheng, “A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,” Measurement, vol. 40, no. 9-10, pp. 943–950, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Sun, G. A. Yang, Q. Chen, A. Palazoglu, and K. Feng, “Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation,” Journal of Vibration and Control, vol. 19, no. 6, pp. 924–941, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. Q. Hu, Z. He, Z. Zhang, and 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
  25. F. He and W. Shi, “WPT-SVMs based approach for fault detection of valves in reciprocating pumps,” in Proceedings of the American Control Conference (ACC '02), vol. 6, pp. 4566–4570, IEEE, Anchorage, Alaska, USA, May 2002. View at Publisher · View at Google Scholar
  26. A. Cury and C. Crémona, “Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts,” Structural Control and Health Monitoring, vol. 19, no. 2, pp. 161–186, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. C. R. Farrar and G. H. James III, “System identification from ambient vibration measurements on a bridge,” Journal of Sound and Vibration, vol. 205, no. 1, pp. 1–18, 1997. View at Publisher · View at Google Scholar · View at Scopus
  28. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713–718, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  30. Z. Sun and C. C. Chang, “Structural damage assessment based on wavelet packet transform,” Journal of Structural Engineering, vol. 128, no. 10, pp. 1354–1361, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at Scopus
  32. E. A. Johnson, H. F. Lam, L. S. Katafygiotis, and J. L. Beck, “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data,” Journal of Engineering Mechanics, vol. 130, no. 1, pp. 3–15, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. S. J. Orfanidis, Optimum Signal Processing. An Introduction, Prentice-Hall, Englewood Cliffs, NJ, USA, 2nd edition, 1996.
  34. C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Tech. Rep., Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2003, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. View at Google Scholar
  35. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” Tech. Rep., Department of Computer Science and Information Engineering, National Taiwan University, 2001, http://www.cs.cmu.edu/~pakyan/compbio/references/Chang_LIBSVM_2001.pdf. View at Google Scholar
  36. J.-G. Han, W.-X. Ren, and Z.-S. Sun, “Wavelet packet based damage identification of beam structures,” International Journal of Solids and Structures, vol. 42, no. 26, pp. 6610–6627, 2005. View at Publisher · View at Google Scholar · View at Scopus