Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A statistical reference-free damage identification for real-time monitoring of truss bridges using wavelet-based log likelihood ratios

  • Lee, Soon Gie (Daewoo Shipbuilding and Marine Engineering Co. LTD.) ;
  • Yun, Gun Jin (Department of Civil Engineering, The University of Akron)
  • Received : 2011.09.11
  • Accepted : 2013.01.27
  • Published : 2013.08.25
⟨ Previous Next ⟩

Abstract

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

Keywords

References

  1. Aktan, A.E., Grimmelsman, K., Ciloglu, K. and Pan, Q. (2004), "Evaluating the reliability of highway bridges following hazards in real-time by structural health monitoring (RT-SHM-D+P)", Nondestruct. Det. Measure. Homeland Security Ii, 5395, 111-121. https://doi.org/10.1117/12.548334
  2. Aldrich, J. (1997), "R. A. Fisher and the making of maximum likelihood 1912-1922", Stat. Sci., 12(3), 162-176. https://doi.org/10.1214/ss/1030037906
  3. Amiri, G.G. and Asadi, A. (2009), "Comparison of different methods of wavelet and wavelet packet transform in processing ground motion records", Int. J. Civil Eng., 7(4), 248-257.
  4. Chang, C.C. and Sun, Z. (2005), "Structural damage localization using spatial wavelet packet signature", Smart Struct. Syst., 1(1), 29-46. https://doi.org/10.12989/sss.2005.1.1.029
  5. Chendeb, M., Khalil, M. and Duchene, J. (2006), "Methodology of wavelet packet selection for event detection", Signal Process., 86(12), 3826-3841. https://doi.org/10.1016/j.sigpro.2006.03.029
  6. Coifman, R.R. and Wickerhauser, M.V. (1992), "Entropy-Based Algorithms for Best Basis Selection", IEEE T. Inform. Theory, 38(2), 713-718. https://doi.org/10.1109/18.119732
  7. Daubechies, I. (1992), Ten Lectures on Wavelets, Philadelphia,PA: Society for Industrial and applied Mathematics.
  8. Ding, Y.L. and Li, A.Q. (2007), "Structural health monitoring of long-span suspension bridges using wavelet packet analysis", Earthq. Eng. Eng. Vib. 6(3), 289-294. https://doi.org/10.1007/s11803-007-0746-y
  9. Grabowska, J., Palacz, M. and Krawczuk, M. (2008), "Damage identification by wavelet analysis", Mech. Syst. Signal Pr., 22(7), 1623-1635. https://doi.org/10.1016/j.ymssp.2008.01.003
  10. Hald, A. (1999), "On the history of maximum likelihood in relation to inverse probability and least squares", Stat. Sci., 14(2), 214-222. https://doi.org/10.1214/ss/1009212248
  11. Han, J.G., Ren, W.X. and Sun, Z.S. (2005), "Wavelet packet based damage identification of beam structures", Int. J. Solids Struct., 42(26), 6610-6627. https://doi.org/10.1016/j.ijsolstr.2005.04.031
  12. Hu, C.S. and Afzal, M.T. (2006), "A wavelet analysis-based approach for damage localization in wood beams", J. Wood Sci., 52(5), 456-460. https://doi.org/10.1007/s10086-005-0783-y
  13. Jiang, X. and Adeli, H. (2007), "Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings", Int. J. Numer. Meth. Eng., 71(5), 606-629. https://doi.org/10.1002/nme.1964
  14. Jiang, X.M. and Mahadevan, S. (2008), "Bayesian wavelet methodology for structural damage detection", Struct. Health Monit., 15(7), 974-991. https://doi.org/10.1002/stc.230
  15. Jiang, X.M., Mahadevan, S. and Adeli, H. (2007), "Bayesian wavelet packet denoising for structural system identification", Struct. Health Monit., 14(2), 333-356. https://doi.org/10.1002/stc.161
  16. Kim, S.B. and Sohn, H. (2007), "Instantaneous reference-free crack detection based on polarization characteristics of piezoelectric materials", Smart Mater. Struct., 16(6), 2375-2387. https://doi.org/10.1088/0964-1726/16/6/042
  17. Liew, K.M. and Wang, Q. (1998), "Application of wavelet theory for crack identification in structures", J. Eng. Mech. -ASCE, 124(2), 152-157. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:2(152)
  18. Masri, S.F., Sheng, L.H., Caffrey, J.P., Nigbor, R.L., Wahbeh, M. and Abdel-Ghaffar, A.M. (2004), "Application of a web-enabled real-time structural health monitoring system for civil infrastructure systems", Smart Mater. Struct., 13(6), 1269-1283. https://doi.org/10.1088/0964-1726/13/6/001
  19. Mood, A.M., Graybill, F.A. and Boes, D.C. (1974), Introduction to the theory of statistics, McGraw-Hill Series in Probability and Statistics, 410.
  20. Newland, D.E. (1993), Random vibrations, spectral and wavelet analysis 3rd Ed., Harlow and John Wiley: New York.
  21. Ni, Y.Q., Zhou, X.T. and Ko, J.M. (2006), "Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks", J. Sound Vib., 290(1-2), 242-263. https://doi.org/10.1016/j.jsv.2005.03.016
  22. Nigbor, R.L. and Diehl, J.G. (1997), "Two years' experience using OASIS real-time remote condition monitoring system on two large bridges", Struct. Health Monit., 410-417.
  23. Ovanesova, A. and Suarez, L.E. (2004), "Applications of wavelet transforms to damage detection in frame structures", Eng. Struct., 26(1), 39-49. https://doi.org/10.1016/j.engstruct.2003.08.009
  24. Shang, S., Yun, G.J., Lee, S.G., Caicedo, J.M. and Narasimhan, S. (2010), "Development of a benchmark laboratory structure for finite element model updating", Proceedings of the 5th International Conference on Bridge Maintenance, Safety and Management. Philadelphia, PA.
  25. Shinde, A. and Hou, Z.K. (2005), "A wavelet packet based sifting process and its application for structural health monitoring", Struct. Health Monit., 4(2), 153-170. https://doi.org/10.1177/1475921705049762
  26. Sun, Z. and Chang, C.C. (2002), "Structural damage assessment based on wavelet packet transform", J. Struct. Eng.-ASCE, 128(10), 1354-1361. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:10(1354)
  27. Sun, Z. and Chang, C.C. (2007), "Vibration based structural health monitoring: wavelet packet transform based solution", Struct. Infrastruct. E., 3(4), 313-323. https://doi.org/10.1080/15732470500473598
  28. Taha, M.M.R., Noureldin, A., Lucero, J.L. and Baca, T.J. (2006), "Wavelet transform for structural health monitoring: A compendium of uses and features", Struct. Health Monit., 5(3), 267-295. https://doi.org/10.1177/1475921706067741
  29. Tsai, C.S., Hsieh, C.T. and Huang, S.J. (2006), "Enhancement of damage-detection of wind turbine blades via CWT-based approaches", IEEE T. Energy Conver., 21(3), 776-781. https://doi.org/10.1109/TEC.2006.875436
  30. Wang, W.J. and McFadden, P.D. (1996), "Application of wavelets to gearbox vibration signals for fault detection", J. Sound Vib., 192(5), 927-939. https://doi.org/10.1006/jsvi.1996.0226
  31. Yen, G.G. and Lin, K.C. (2000), "Wavelet packet feature extraction for vibration monitoring", IEEE T. Ind. Electron., 47(3), 650-667. https://doi.org/10.1109/41.847906
  32. Yun, G.J. (2010), Collaboratory Website. http://isehm.mech.uakron.edu:8084/Benchmark .
  33. Yun, G.J., Lee, S.G., Carletta, J. and Nagayama, T. (2011), "Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors", Eng. Struct., 33, 2162-2172. https://doi.org/10.1016/j.engstruct.2011.03.007
  34. Zhou, Z.J., Hu, C.H., Han, X.X. and Chen, G.J. (2005), "Adaptive wavelet packet neural network based fault diagnosis for missile's amplifier", Adv. Neural Networks - Isnn 2005, Pt 3, Proceedings, 3498, 591-596.

Cited by

  1. Nonlinear damage detection using higher statistical moments of structural responses vol.54, pp.2, 2015, https://doi.org/10.12989/sem.2015.54.2.221
  2. Experimental validation of multistep quantitative crack damage assessment for truss structures by finite element model updating vol.23, pp.12, 2014, https://doi.org/10.1088/0964-1726/23/12/125034