Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Signal processing based damage detection in structures subjected to random excitations

  • Montejo, Luis A. (Department of Engineering Science and Materials, University of Puerto Rico at Mayaguez)
  • Received : 2010.09.13
  • Accepted : 2011.10.12
  • Published : 2011.12.25
⟨ Previous Next ⟩

Abstract

Damage detection methodologies based on the direct examination of the nonlinear-nonstationary characteristics of the structure dynamic response may play an important role in online structural health monitoring applications. Different signal processing based damage detection methodologies have been proposed based on the uncovering of spikes in the high frequency component of the structural response obtained via Discrete Wavelet transforms, Hilbert-Huang transforms or high pass filtering. The performance of these approaches in systems subjected to different types of excitation is evaluated in this paper. It is found that in the case of random excitations, like earthquake accelerations, the effectiveness of such methodologies is limited. An alternative damage detection approach using the Continuous Wavelet Transform (CWT) is also evaluated to overcome this limitation. Using the CWT has the advantage that the central frequencies at which it operates can be defined by the user while the frequency bands of the detail functions obtained via DWT are predetermined by the sampling period of the signal.

Keywords

References

  1. Al-Khalidy, A., Noori, M., Hou, Z., Yamamoto, S., Masuda, A. and Sone, A. (1997), "Health monitoring systems of linear structures using wavelet analysis", Proceeding of the 1st International Workshop on Structural Health Monitoring, Stanford, CA.
  2. Bisht, S.S. (2005), "Methods for structural health monitoring and damage detection of civil and mechanical systems", MS Thesis, Department of Engineering Science and Mechanics, Virginia Polytechnic Institute and State University.
  3. Carmona, R.A., Hwang, W.L. and Torresani, B. (1997), "Characterization of signals by the ridges of their wavelet transforms", IEEE T. Signal Proc., 54(10), 2586-2590.
  4. Clinton, J.F., Bradford, S.C., Heaton, T.H. and Favela, J. (2006), "The observed wander of the natural frequencies in a structure", B. Seismol. Soc. Am., 96(1), 237-257. https://doi.org/10.1785/0120050052
  5. 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 vibration characteristics: a literature review, Los Alamos National Laboratory Report LA-13070-MS.
  6. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Dig., 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  7. EERI Earthquake Engineering Research Institute (2009), "Learning from earthquakes: The M6.3 Abruzzo, Italy, Earthquake of April 6, 2009", EERI Special Earthquake Report.
  8. Farrar, C.R. and Cone, K.M. (1995), "Vibration testing of the 1-40 bridge before and after the introduction of damage", Proceedings 73th international Modal Analysis Conference, Nashville, TN.
  9. Farrar, C.R., Doebling, S.W. and Nix DA. (2001), "Vibration based structural damage identification", Philos. T. R. Soc. A, 359,131-149. https://doi.org/10.1098/rsta.2000.0717
  10. Hou, Z. and Noori, M. (1999), "Application of wavelet analysis for structural health monitoring", Proceedings of the 2nd International Workshop on Structural Health Monitoring, Stanford, CA.
  11. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Philos. T. R. Soc. A, 454, 903-995.
  12. Huang, N.E., Shen, Z. and Long, S.R. (1999), "A new view of nonlinear water waves: the Hilbert spectrum", Annu. Rev. Fluid Mech., 31, 417-457. https://doi.org/10.1146/annurev.fluid.31.1.417
  13. Huang, N.E. (2005), "Introduction to the Hilbert Huang transform and its related mathematical problems", Hilbert Huang Transform and Its Applications, World Scientific, 1-26.
  14. Huth, O., Feltrin, G., Maeck, J., Kilic, N. and Motavalli, M. (2005), "Damage identification using modal data: experiences on a prestressed concrete bridge", ASCE J. Struct. Eng., 131(12), 1898-1910. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:12(1898)
  15. Kijewski, T. and Kareem, A. (2003), "Wavelets transforms for system identification in civil engineering", Comput. Aided Civil Infra. Eng., 18, 339-355. https://doi.org/10.1111/1467-8667.t01-1-00312
  16. Kijewski-Correa, T. and Kareem, A. (2006), "Efficacy of Hilbert and Wavelet transforms for time-frequency analysis", J. Eng. Mech., 132(10), 1037-1049. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:10(1037)
  17. Mallat, S.G. (1989), "Theory for multiresolution signal decomposition: the wavelet representation", IEEE T. Pattern Anal., 11(7), 674-693. https://doi.org/10.1109/34.192463
  18. Montejo, L.A. and Kowalsky, M.J. (2008), "Estimation of frequency-dependent strong motion duration via wavelets and its influence on nonlinear seismic response", Comput. Aided Civil Infra. Eng., 23, 253-264. https://doi.org/10.1111/j.1467-8667.2007.00534.x
  19. Montejo, L.A. and Suarez, L.E. (2007), "Applications of the Wavelet Transform in structural engineering", Mecanica Computacional, XXVI, 2742-2753. (in Spanish)
  20. Ovanesova, A.V. and Suarez, L.E. (2004), "Applications of wavelet transforms to damage detection of frame structures", Eng. Struct., 26, 39-49. https://doi.org/10.1016/j.engstruct.2003.08.009
  21. PEER Pacific Earthquake Engineering Research Center (2010), Next Generation Attenuation of Ground Motions NGA database.
  22. Rilling, G., Flandrin, P. and Gonçalvès, P. (2003), "On empirical mode decomposition and its algorithms", IEEEEURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03.
  23. Rodgers, J.E. and Celebi, M. (2006), "Seismic response and damage detection analyses of an instrumented steel moment-framed building", ASCE J. Struct. Eng., 132(10), 1543-1552. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:10(1543)
  24. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W. and Nadler, B.R. (2003), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory Report LA-13976-MS.
  25. Sone, A., Yamamoto, S., Nakaoka, A., Masuda, A. (1995), "Health monitoring system of structures based on orthonormal wavelet transform" Proceedings of the ASME Pressure Vessels and Piping Conference, 161-167.
  26. Todorovska, M.I. (2001), "Estimation of instantaneous frequency of signals using the continuous wavelet transform," Report CE 01-07, Department of Civil Engineering, University of Southern California
  27. Todorovska, M.I. and Trifunac, M.D. (2007), "Earthquake damage detection in the imperial county services building I: the data and time frequency analysis", Soil Dyn. Earthq. Eng., 27, 564-576. https://doi.org/10.1016/j.soildyn.2006.10.005
  28. Tododrovska, M.I. (2009), "Earthquake damage detection in buildings and early warning based on wave travel times", Proceedings of 2009 NSF Engineering Research and Innovation Conference, Honolulu, Hawaii.
  29. Todorovska, M.I. and Trifunac, M.D. (2010), "Earthquake damage detection in the Imperial County Services building II: Analysis of novelties via wavelets", Struct. Control Hlth. Monit., 17(8), 895-917. https://doi.org/10.1002/stc.350
  30. Vincent, H.T., Hu. S.J. and Hou, Z. (1999), "Damage detection using empirical mode decomposition and a comparison with wavelet analysis", Proceedings of the 2nd International Workshop in Structural Health Monitoring, Stanford, CA.
  31. Xu, Y.L. and Chen, J. (2004) "Structural damage detection using empirical mode decomposition: experimental investigation", J. Eng. Mech., 130(11), 1279-1288. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:11(1279)
  32. Yan, B. and Miyamoto, A. (2006), "A comparative study of modal parameter identification based on Wavelet and Hilbert-Huang transforms", Comput. Aided Civil Infra. Eng., 21, 9-23. https://doi.org/10.1111/j.1467-8667.2005.00413.x
  33. Yang, J.N., Lei, Y., Lin, S. and Huang, N. (2004), "Hilbert-Huang based approach for structural damage detection", J. Eng. Mech., 130(1), 85-95. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(85)

Cited by

  1. Synchrosqueezed wavelet transform for frequency and damping identification from noisy signals vol.9, pp.5, 2012, https://doi.org/10.12989/sss.2012.9.5.441
  2. Dynamic analysis and shear connector damage identification of steel-concrete composite beams vol.13, pp.4, 2012, https://doi.org/10.12989/scs.2012.13.4.327
  3. On the use of numerical models for validation of high frequency based damage detection methodologies vol.2, pp.4, 2015, https://doi.org/10.12989/smm.2015.2.4.383
  4. Damping and frequency changes induced by increasing levels of inelastic seismic demand vol.14, pp.3, 2014, https://doi.org/10.12989/sss.2014.14.3.445
  5. Wavelet-Based Damage Detection in Reinforced Concrete Structures Subjected to Seismic Excitations vol.17, pp.8, 2013, https://doi.org/10.1080/13632469.2013.804467
  6. Experimental and numerical evaluation of wavelet based damage detection methodologies vol.7, pp.1, 2015, https://doi.org/10.1007/s40091-015-0084-7
  7. Optimal Wavelet Parameters for System Identification of Civil Engineering Structures vol.34, pp.1, 2018, https://doi.org/10.1193/092016EQS154M
  8. Selection of optimal noise filtering technique for guided waves in diagnosis of structural cracks vol.9, pp.2, 2018, https://doi.org/10.1108/IJSI-05-2017-0031
  9. Damage Detection in Initially Nonlinear Structures Based on Variational Mode Decomposition vol.20, pp.10, 2011, https://doi.org/10.1142/s0219455420420092
  10. Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors vol.20, pp.6, 2011, https://doi.org/10.3390/s20061731
  11. A System Identification-Based Damage-Detection Method for Gravity Dams vol.2021, pp.None, 2021, https://doi.org/10.1155/2021/6653254