Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

On the use of multivariate autoregressive models for vibration-based damage detection and localization

  • Achilli, Alessandra (Department DICAM, University of Bologna) ;
  • Bernagozzi, Giacomo (Department DICAM, University of Bologna) ;
  • Betti, Raimondo (Department of Civil Engineering and Engineering Mechanics, Columbia University) ;
  • Diotallevi, Pier Paolo (Department DICAM, University of Bologna) ;
  • Landi, Luca (Department DICAM, University of Bologna) ;
  • Quqa, Said (Department DICAM, University of Bologna) ;
  • Tronci, Eleonora M. (Department of Civil Engineering and Engineering Mechanics, Columbia University)
  • Received : 2020.07.07
  • Accepted : 2020.10.03
  • Published : 2021.02.25
⟨ Previous Next ⟩

Abstract

This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different locations of the analyzed structure. Outlier analysis and statistical pattern recognition are exploited for damage detection and localization. In particular, the Mahalanobis distance between a set of reference (i.e., "healthy") and inspection parameters is evaluated. A threshold is then selected to determine whether the inspection vectors refer to damaged or undamaged conditions. The effectiveness of the proposed approach is proved using numerical simulations and experimental data from a benchmark test. The analysis results show that the largest values of Mahalanobis distance can be found in the proximity of those sensors closest to the damaged elements. Thus, the Mahalanobis distance applied to vectors of multivariate autoregressive parameters has proven to be a robust indicator for damage detection and localization.

Keywords

Acknowledgement

The authors would like to acknowledge the Engineering Institute at Los Alamos National Laboratory for making available to the public domain the experimental data used in this work, and downloadable from http://institute.lanl.gov/ei/software-and-data/.

References

  1. Akaike, H. (1974), "A new look at the statistical model identification", IEEE Transact. Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Balsamo, L. and Betti, R. (2015), "Data-based structural health monitoring using small training datasets", Struct. Control Health Monitor., 22(10), 1240-1264. https://doi.org/10.1002/stc.1744
  3. Barnett, V. and Lewis, T. (1994), Outliers in Statistical Data, (3rd Edition), John Wiley & Sons, Chichester, UK.
  4. Bernagozzi, G., Mukhopadhyay, S., Betti, R., Landi, L. and Diotallevi, P.P. (2018), "Output-only damage detection in buildings using proportional modal flexibility-based deflections in unknown mass scenarios", Eng. Struct., 167, 549-566. https://doi.org/10.1016/j.engstruct.2018.04.036
  5. Bodeux, J.B. and Golinval, J.C. (2003), "Modal identification and damage detection using the data-driven stochastic subspace and VARMA methods", Mech. Syst. Signal Process., 17(1), 83-89. https://doi.org/10.1006/mssp.2002.1543
  6. Brincker, R. and Ventura, C.E. (2015), Introduction to Operational Modal Analysis, John Wiley & Sons, Chichester, UK.
  7. Das, S., Saha, P. and Patro, S.K. (2016), "Vibration-based damage detection techniques used for health monitoring of structures: a review", J. Civil Struct. Health Monitor., 6, 477-507. https://doi.org/10.1007/s13349-016-0168-5
  8. De Stefano, A., Sabia, D. and Sabia, L. (1997), "Structural identification using VARMA models from noisy dynamic response under unknown random excitation", Proceedings of International Workshop on Damage Assessment Using Advanced Signal Processing Procedures DAMAS 97, Sheffield, UK, June, pp. 419-429.
  9. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: a review and comparative study", Struct. Health Monitor., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  10. Farrar, C.R. and Worden, K. (2007), "An introduction to structural health monitoring", Philosophical Transactions of the Royal Society A, Mathematical, Physical and Engineering Sciences, 365(1851), 303-315. https://doi.org/10.1098/rsta.2006.1928
  11. Farrar, C.R. and Worden, K. (2013), Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons, Chichester, UK.
  12. Figueiredo, E., Park, G., Figueiras, J., Farrar, C.R. and Worden, K. (2009), "Structural Health Monitoring Algorithm Comparisons Using Standard Datasets", Report LA-14393; Los Alamos National Laboratory, Los Alamos, NM, USA.
  13. Goi, Y. and Kim C.W. (2017), "Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model", J. Civil Struct. Health Monitor., 7, 153-162. https://doi.org/10.1007/s13349-017-0222-y
  14. Guidorzi, R. (2003), Multivariable System Identification. From Observations to Models, Bononia University Press, Bologna, Italy.
  15. Guidorzi, R., Losito, M. and Muratori, T. (1982), "The range error test in the structural identification of linear multivariable systems", IEEE Transact. Automatic Control, 27(5), 1044-1054. https://doi.org/10.1109/TAC.1982.1103068
  16. Guidorzi, R., Diversi, R., Vincenzi, L., Mazzotti, C. and Simioli, V. (2014), "Structural monitoring of a tower by means of MEMS-based sensing and enhanced autoregressive models", Eur. J. Control, 20(1), 4-13. https://doi.org/10.1016/j.ejcon.2013.06.004.
  17. Heyns, P.S. (1997), "Structural damage assessment using response-only measurements", Proceedings of International Workshop on Damage Assessment Using Advanced Signal Processing Procedures DAMAS 97, Sheffield, UK, June, pp. 213-223.
  18. Ljung, L. (1987), System Identification, Theory for the User, Prentice-Hall, Inc, Englewood Cliffs, NJ, USA.
  19. Loh, C.H., Chan, C.K., Chen, S.F. and Huang, S.K. (2016), "Vibration-based damage assessment of steel structure using global and local response measurements", Earthq. Eng. Struct. Dyn., 45, 699-718. https://doi.org/10.1002/eqe.2680
  20. Mahalanobis, P.C. (1936), "On the generalized distance in statistics", Proceedings of the National Institute of Sciences of India, 2(1), 49-55.
  21. Mosavi, A.A., Dickey, D., Seracino, R. and Rizkalla, S. (2012), "Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances", Mech. Syst. Signal Process., 26, 254-267. https://doi.org/10.1016/j.ymssp.2011.06.009
  22. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291, 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  23. Roy, K., Bhattacharya, B. and Ray-Chaudhuri, S. (2015), "ARX model-based damage sensitive features for structural damage localization using output-only measurements", J. Sound Vib., 349, 99-122. https://doi.org/10.1016/j.jsv.2015.03.038
  24. Schwarz, G.E. (1978), "Estimating the dimension of a model", Annals Statist., 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
  25. Simani, S., Fantuzzi, C. and Patton, R.J. (2003), Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Springer-Verlag London.
  26. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R. and Czarnecki, J.J. (2004), "A review of structural health monitoring literature: 1996-2001", Report LA-13976-MS; Los Alamos National Laboratory, Los Alamos, NM, USA.
  27. Wang, Z. and Ong, K.C.G. (2008), "Autoregressive coefficients based Hotelling's T2 control chart for structural health monitoring", Comput. Struct., 86, 1918-1935. https://doi.org/10.1016/j.compstruc.2008.02.007
  28. Worden, K., Manson, G. and Fieller, N.R.J. (2000), "Damage detection using outlier analysis", J. Sound Vib., 229(3), 647-667. https://doi.org/10.1006/jsvi.1999.2514