DOI QR코드

DOI QR Code

HHT method for system identification and damage detection: an experimental study

  • Zhou, Lily L. (College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics) ;
  • Yan, Gang (College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics)
  • Received : 2005.04.07
  • Accepted : 2006.01.09
  • Published : 2006.04.25

Abstract

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

Keywords

References

  1. Chang, F.-K. (ed.) (1997, 1999, 2001, 2003), Proceedings of 1st, 2nd, 3rd and 4th International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA
  2. Doebling, S. W., Farrar, C. R., Prime, M. B. and Shevitz D. W (1996), 'Damage identification and health monitoring of structural and mechanical systems from changes in their characteristics: a literature review', Report LA-13070-MS, Los Alamos National Laboratory, Los Alamos, NM
  3. Dyke, S. J., Caicedo, J. M. and Johnson, E. (2000), 'Monitoring of a benchmark structure for damage identification', Proceedings of 14th ASCE Engineering Mechanics Conference, Austin, TX
  4. Feldman, M. (1994), 'Non-linear system vibration analysis using Hilbert transform-I: free vibration analysis method FREEVIB', Mechanical Systems and Signal Processing, 8(2), 119-127 https://doi.org/10.1006/mssp.1994.1011
  5. Feldman, M. (1997), 'Nonliear free-vibration identification via the Hilbert transform', J. Sound Vib., 208(3), 475-489 https://doi.org/10.1006/jsvi.1997.1182
  6. Gurley, K. and Kareem A. (1999), 'Application of wavelet transform in earthquake, wind, and ocean engineering', J. Eng. Struct., 21, 149-167 https://doi.org/10.1016/S0141-0296(97)00139-9
  7. Hou, Z., Noori, M. and Amand, R. S. (2000), 'Wavelet-based approach for structural damage detection', J. Eng. Mech., ASCE, 126(7), 677-683 https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(677)
  8. Housner, G. W., Bergman, L. A., Garghey, T. K. et al. (1997), 'Structural control: past, present and future'. J. Eng. Mech.: Special Issue, ASCE, 123(9), 897-958 https://doi.org/10.1061/(ASCE)0733-9399(1997)123:9(897)
  9. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H. et al. (1998), 'The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis', Proceedings of the Royal Society of London. Series A, 454, 903-995
  10. Huang, N. E., Shen, Z. and Long, S. R. (1999), 'A new view of nonlinear water waves: the Hilbert spectrum', Annual Review of Fluid Mech., 31, 417-457 https://doi.org/10.1146/annurev.fluid.31.1.417
  11. Ruzzene, M., Fasana, A., Baribaldi, L. et al. (1997), 'Natural frequencies and damping identification using wavelet transform: application to real data', Mechanical Systems and Signal Processing, 11(2), 207-218 https://doi.org/10.1006/mssp.1996.0078
  12. Staszewski, W. J. (1997), 'Identification of damping in MDOF systems using time-scale decomposition', J. Sound Vib., 203(2), 283-305 https://doi.org/10.1006/jsvi.1996.0864
  13. Yang, J. N. and Lei, Y. (1999), 'Identification of natural frequencies and damping ratios of linear structures via Hilbert transform and empirical mode decomposition', Proceedings of the IASTED International Conference on Intelligent Systems and Control, Santa Barbara, CA
  14. Yang, J. N., and Lei, Y. (2000), 'Identification of civil structures with non-proportional damping', Proceedings of SPIE, Smart Structures and Materials, Newport Beach, CA
  15. Yang, J. N., Lei, Y. and Huang, N. (2001), 'Damage identification of civil engineering structures using Hilbert-Huang transform', Proceedings of 3rd International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA
  16. Yang, J. N., Lin, S. and Pan, S. (2002), 'Damage identification of structures using Hilbert-Huang spectral analysis', Proceedings of 15th ASCE Engineering Mechanics Conference, Columbia University, New York, NY
  17. Yang, J. N., Lei, Y. and Huang, N. (2003a), 'Identification of linear structures based on Hilbert-Huang spectral analysis. Part I: normal modes', J. Earthq. Eng. Struct. Dyn., 32, 1443-1467 https://doi.org/10.1002/eqe.287
  18. Yang, J. N., Lei, Y. and Huang, N. (2003b), 'Identification of linear structures based on Hilbert-Huang spectral analysis. Part II: complex modes', J. Earthq. Eng. Struct. Dyn., 32, 1533-1554 https://doi.org/10.1002/eqe.288
  19. Yang, J. N., Lei, Y., Lin, S. and Huang N. (2004), 'Hilbert-Huang based approach for structural damage detection', J. Eng. Mech., ASCE, 130(1), 85-95 https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(85)

Cited by

  1. Parametric identification of a cable-stayed bridge using least square estimation with substructure approach vol.15, pp.2, 2015, https://doi.org/10.12989/sss.2015.15.2.425
  2. Damage identification of substructure for local health monitoring vol.4, pp.6, 2008, https://doi.org/10.12989/sss.2008.4.6.795
  3. Damage detection by means of structural damping identification vol.30, pp.12, 2008, https://doi.org/10.1016/j.engstruct.2008.05.024
  4. Multiagent-Based Collaborative Framework for a Self-Managing Structural Health Monitoring System vol.26, pp.1, 2012, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000107
  5. Experimental study on relationship between processing parameters and stress wave propagation during automated fiber placement process vol.213, 2017, https://doi.org/10.1088/1757-899X/213/1/012031
  6. Modeling Lamb Wave Propagation in Damaged Structures Based upon Spectral Element Method vol.570, pp.1662-8985, 2012, https://doi.org/10.4028/www.scientific.net/AMR.570.79
  7. Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation vol.19, pp.3, 2019, https://doi.org/10.3390/s19030463
  8. A comparative study on the subspace based system identification techniques applied on civil engineering structures vol.7, pp.2, 2006, https://doi.org/10.12989/sss.2011.7.2.153
  9. Numerical studies on the effect of measurement noises on the online parametric identification of a cable-stayed bridge vol.19, pp.3, 2006, https://doi.org/10.12989/sss.2017.19.3.259