Skip to main content
SpringerLink
Log in

Stochastic Wavenumber Estimation: Damage Detection Through Simulated Guided Lamb Waves

  • Conference paper
  • First Online:
Rotating Machinery, Hybrid Test Methods, Vibro-Acoustics & Laser Vibrometry, Volume 8

  • 1244 Accesses

Abstract

Acoustic Wavenumber Spectroscopy (AWS) is a technique for nondestructive testing and evaluation capable of identifying local damage in thin plates through the estimation of the characteristic wavenumber of propagating elastic waves. Current state of the art in AWS estimates wavenumber based on the maximum data fit of the wavenumber dispersion curve and derives thickness deterministically through the Lamb wave equations. Successful determination of thickness from the measurements through inverse analysis is dependent upon two aspects: uncertainties regarding material properties of the system (parametric uncertainty) and uncertainties regarding data collected in the field under less than ideal conditions (experimental uncertainty). Thus, the deterministic approach may lead to large false positives in the presence of parametric and experimental uncertainties. The focus of this paper is to develop a stochastic approach for inferring thickness from the measurements in which both parametric and experimental uncertainties are accounted for. Herein, parametric uncertainty is managed by calibrating material-dependent properties using wavenumber measurements. Experimental uncertainty is controlled through incorporation of expert judgment by means of an elicited prior uncertainty of thickness. The technological advancement produced in this study is demonstrated on a case study application of an aluminum plate with imposed thinning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 365(1851), 303–315 (2007)

    Article  Google Scholar 

  2. Rytter, A.: Vibration based inspection of civil engineering structures. Ph.D., Aalborg University, Denmark (1993)

    Google Scholar 

  3. Atamturktur, H.S., Gilligan, C.R., Salyards, K.A.: Detection of internal defects in concrete members using global vibration characteristics. ACI Mater. J. 110(5), (2013)

    Google Scholar 

  4. Doebling, S.W., Farrar, C.R., Prime, M.B., Shevitz, D.W.: Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review. Los Alamos National Lab., NM (United States) (1996)

    Book  Google Scholar 

  5. Sohn, H.: A Bayesian probabilistic approach for structure damage detection (1997)

    Google Scholar 

  6. Alampalli, S.: Effects of testing, analysis, damage, and environment on modal parameters. Mech. Syst. Signal Process. 14(1), 63–74 (2000)

    Article  Google Scholar 

  7. Stubbs, N., Kim, J.T.: Damage localization in structures without baseline modal parameters. AIAA J. 34(8), 1644–1649

    Google Scholar 

  8. Friswell, M.I.: Damage identification using inverse methods. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 365(1851), 393–410 (2007)

    Article  Google Scholar 

  9. Prabhu, S., Atamturktur, S.: Selection of optimal sensor locations based on modified effective independence method: case study on a gothic revival cathedral. J. Archit. Eng. 19(4), 288–301 (2013)

    Article  Google Scholar 

  10. Nataraja, R.: Structural integrity monitoring in real seas. In: Proceedings of the 15th Annual Offshore Technology Conference, pp. 221–228 (1983)

    Google Scholar 

  11. Atamturktur, S., Bornn, L., Hemez, F.: Vibration characteristics of vaulted masonry monuments undergoing differential support settlement. Eng. Struct. 33(9), 2472–2484 (2011)

    Article  Google Scholar 

  12. Beck, J.L., Au, S.K., Vanik, M.W.: A Bayesian probabilistic approach to structural health monitoring. In: American Control Conference, 1999. Proceedings of the 1999, vol. 2, pp. 1119–1123 (1999)

    Google Scholar 

  13. Atamturktur, S., Pavic, A., Reynolds, P., Boothby, T.: Full-scale modal testing of vaulted gothic churches: lessons learned. Exp. Tech. 33(4), 65–74 (2009)

    Article  Google Scholar 

  14. Kino, G.S.: Acoustic imaging for nondestructive evaluation. Proc. IEEE 67(4), 510–525 (1979)

    Article  Google Scholar 

  15. Croxford, A.J., Wilcox, P.D., Drinkwater, B.W., Konstantinidis, G.: Strategies for guided-wave structural health monitoring. Proc. R. Soc. Math. Phys. Eng. Sci. 463(2087), 2961–2981 (2007)

    Article  Google Scholar 

  16. Flynn, E.B., Chong, S.Y., Jarmer, G.J., Lee, J.-R.: Structural imaging through local wavenumber estimation of guided waves. NDT E Int. 59, 1–10 (2013)

    Article  Google Scholar 

  17. Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation. Siam (2005)

    Google Scholar 

  18. Huang, Q., Gardoni, P., Hurlebaus, S.: A probabilistic damage detection approach using vibration-based nondestructive testing. Struct. Saf. 38, 11–21 (2012)

    Article  Google Scholar 

  19. Lee, J.-R., Jeong, H., Ciang, C.C., Yoon, D.-J., Lee, S.-S.: Application of ultrasonic wave propagation imaging method to automatic damage visualization of nuclear power plant pipeline. Nucl. Eng. Des. 240(10), 3513–3520 (2010)

    Article  Google Scholar 

  20. Lamb, H.: On waves in an elastic plate. Proc. R. Soc. Lond., 114–128 (1917)

    Google Scholar 

  21. Achenback, J.: Wave Propagation in Elastic Solids. Elsevier (1984)

    Google Scholar 

  22. Lee, J.-R., Yenn Chong, S., Jeong, H., Kong, C.-W.: A time-of-flight mapping method for laser ultrasound guided in a pipe and its application to wall thinning visualization. NDT E Int. 44(8), 680–691 (2011)

    Article  Google Scholar 

  23. Monchalin, J.P.: Non contact generation and detection of ultrasound with lasers. In: Proceedings of the 16th World Conference on Nondestructive Testing, pp. 1–9 (2004)

    Google Scholar 

  24. Draper, D.: Assessment and propagation of model uncertainty. J. R. Stat. Soc. Ser. B Methodol. 57(1), 45–97 (1995)

    MathSciNet  MATH  Google Scholar 

  25. Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B Stat. Methodol. 63(3), 425–464 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  26. Farajpour, I., Atamturktur, S.: Error and uncertainty analysis of inexact and imprecise computer models. J. Comput. Civ. Eng. 27(4), 407–418 (2013)

    Article  Google Scholar 

  27. Higdon, D., Gattiker, J., Williams, B., Rightley, M.: Computer model calibration using high-dimensional output. Am. Stat. Assoc. 103(482), 570–583 (2007)

    Google Scholar 

  28. Unal, C., Williams, B., Hemez, F., Atamturktur, S.H., McClure, P.: Improved best estimate plus uncertainty methodology, including advanced validation concepts, to license evolving nuclear reactors. Nucl. Eng. Des. 241(5), 1813–1833 (2011)

    Article  Google Scholar 

  29. Bayarri, M.J., Berger, J.O., Paulo, R., Sacks, J., Cafeo, J.A., Cavendish, J., Lin, C.-H., Tu, J.: A framework for validation of computer models. Technometrics 49(2), 138–154 (2007)

    Article  MathSciNet  Google Scholar 

  30. Higdon, D., Kennedy, M., Cavendish, J.C., Cafeo, J.A., Ryne, R.D.: Combining field data and computer simulations for calibration and prediction. SIAM J. Sci. Comput. 26(2), 448–466 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  31. Bastos, L.S., O’Hagan, A.: Diagnostics for Gaussian process emulators. Technometrics 51(4), 425–438 (2009)

    Article  MathSciNet  Google Scholar 

  32. DiazDelaO, F.A., Adhikari, S.: Structural dynamic analysis using Gaussian process emulators. Eng. Comput. 27(5), 580–605 (2010)

    Article  MATH  Google Scholar 

  33. Van Buren, K.L., Atamturktur, S., Hemez, F.M.: Model selection through robustness and fidelity criteria: modeling the dynamics of the CX-100 wind turbine blade. Mech. Syst. Signal Process. 43(1–2), 246–259 (2014)

    Article  Google Scholar 

  34. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J.Chem. Phys. 21(6), 1087 (1953)

    Article  Google Scholar 

  35. Gelfand, A.E., Smith, A.E.: Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85(412), 398–409

    Google Scholar 

  36. Mitchell, M.: An Introduction to Genetic Algorithms, 3rd edn. MIT, Cambridge, MA (1998)

    MATH  Google Scholar 

  37. Gannon, A., Wheeler, E., Brown, K., Flynn, E.B., Warren, W.: A high-speed dual-stage ultrasonic guided wave system for localization and characterization of defects. In: Structural Health Monitoring and Damage Detection, vol. 7, pp. 123–136 (2015)

    Google Scholar 

  38. Flynn, E.B., Lee, J.R., Jarmer, G.J., Park, G.: Frequency-wavenumber processing of laser-excited guided waves for imaging structural features and defects. In: 6th European Workshop on Structural Health Monitoring (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA

    Garrison N. Stevens & Sez Atamturktur

  2. Engineering Institute, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Kendra L. Van Buren & Eric B. Flynn

  3. Department of Aerospace Engineering, KAIST University, Daejeon, South Korea

    Jung-Ryul Lee

Authors
  1. Garrison N. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kendra L. Van Buren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eric B. Flynn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sez Atamturktur
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jung-Ryul Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kendra L. Van Buren .

Editor information

Editors and Affiliations

  1. Mechanical Engineering, Michigan Technological University Mechanical Engineering, Houghton, Michigan, USA

    James De Clerck

  2. Sandia National Laboratory , Albuquerque, USA

    David S. Epp

Rights and permissions

Reprints and permissions

Copyright information

© 2016 The Society for Experimental Mechanics, Inc.

About this paper

Cite this paper

Stevens, G.N., Van Buren, K.L., Flynn, E.B., Atamturktur, S., Lee, JR. (2016). Stochastic Wavenumber Estimation: Damage Detection Through Simulated Guided Lamb Waves. In: De Clerck, J., Epp, D. (eds) Rotating Machinery, Hybrid Test Methods, Vibro-Acoustics & Laser Vibrometry, Volume 8. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-30084-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30084-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30083-2

  • Online ISBN: 978-3-319-30084-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation