Structural Engineering and Mechanics

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Holder exponent analysis for discontinuity detection

  • Sohn, Hoon (Weapon Response Group, Engineering Sciences and Applications Division, Los Alamos National Laboratory) ;
  • Robertson, Amy N. (Weapon Response Group, Engineering Sciences and Applications Division, Los Alamos National Laboratory) ;
  • Farrar, Charles R. (Weapon Response Group, Engineering Sciences and Applications Division, Los Alamos National Laboratory)
  • Received : 2002.10.01
  • Accepted : 2003.08.27
  • Published : 2004.03.25
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Abstract

In this paper, a Holder exponent, a measure of the degree to which a signal is differentiable, is presented to detect the presence of a discontinuity and when the discontinuity occurs in a dynamic signal. This discontinuity detection has potential applications to structural health monitoring because discontinuities are often introduced into dynamic response data as a result of certain types of damage. Wavelet transforms are incorporated with the Holder exponent to capture the time varying nature of discontinuities, and a classification procedure is developed to quantify when changes in the Holder exponent are significant. The proposed Holder exponent analysis is applied to various experimental signals to reveal underlying damage causing events from the signals. Signals being analyzed include acceleration response of a mechanical system with a rattling internal part, acceleration signals of a three-story building model with a loosing bolt, and strain records of an in-situ bridge during construction. The experimental results presented in this paper demonstrate that the Holder exponent can be an effective tool for identifying certain types of events that introduce discontinuities into the measured dynamic response data.

Keywords

References

  1. Brownjohn, J.M.W. and Moyo, P. (2000), "Monitoring of Singapore-Malaysia Second Link during construction", Proc. of the 2nd Int. Conf. on Experimental Mechanics, Singapore, Nov. 29-Dec. 1, 528-533.
  2. Hambaba, A. and Huff, A.E. (2000), "Multiresolution error detection on early fatigue cracks in gears", IEEEAerospace Conference Proceedings, 6, 367-372.
  3. 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. (1998),"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time seriesanalysis", Proc. R. Soc. Lond. A, 454, 903-995. https://doi.org/10.1098/rspa.1998.0193
  4. Mallat, S. and Hwang, W.L. (1992), "Singularity detection and processing with wavelets", IEEE Transactions onInformation Theory, 38, 617-643. https://doi.org/10.1109/18.119727
  5. Peng, Z., He, Y., Chen, Z. and Chu, F. (2002), "Identification of the shaft orbit for rotating machines usingwavelet modulus maxima", Mechanical Systems and Signal Processing, 16(4), 623-635. https://doi.org/10.1006/mssp.2002.1494
  6. Robertson, A.H., Farrar, C.R. and Sohn, H. (2003a), "Singularity detection for structural health monitoring usingholder exponents", Mechanical Systems and Signal Processing, 17(6), 1163-1184. https://doi.org/10.1006/mssp.2002.1569
  7. Robertson, A.N., Sohn, H. and Farrar, C.R. (2003b), "An improved statistical classifier for identifying signaldiscontinuities using holder exponents", The 4th International Workshop on Structural Health Monitoring,September 13-17, Stanford, CA.
  8. Shekarforoush, H., Zerubia, J. and Berthod, M. (1998), "Denoising by extracting fraction order singularities",IEEE International Conference on Acoustics, Speech and Signal Processing, 5, 2889-2892.
  9. Sohn, H., Czarnecki, J.J. and Farrar, C.R. (2000), "Structural health monitoring using statistical process control",J. Struct. Eng., ASCE, 126(11), 1356-1363. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:11(1356)
  10. Sohn, H. and Farrar, C.R. (2001), "Damage diagnosis using time series analysis of vibration signals", SmartMaterials and Structures, 10, 446-451. https://doi.org/10.1088/0964-1726/10/3/304
  11. Sohn, H., Farrar, C.R., Hemez, F.M., Czarnecki, J.J., Shunk, D.D., Stinemates, D.W. and Nadler B.R. (2003), "Areview of structural health monitoring literature: 1996-2001", Los Alamos National Laboratory Report, LA-13976-MS, 2003.
  12. Struzik, A. (2001), "Wavelet methods in (financial) time-series processing", Physica A, 296, 307-319. https://doi.org/10.1016/S0378-4371(01)00101-7
  13. Wong, K.Y., Chan, W.Y., Man, K.L., Mak, W.L.N. and Lau, C.K. (2000), "Structural health monitoring resultson Tsing Ma, Kap Shui Mun, and Ting Kau Bridges", Proceedings of SPIE: Nondestructive Evaluation ofHighways, Utilities, and Pipelines (Vol. 3995), Newport, CA.

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