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Stochastic System Identification for Operational Modal Analysis: A Review

Peeters, Bart, De Roeck, Guido·2001·Journal of Dynamic Systems, Measurement, and Control
DOI 10.1115/1.1410370
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Abstract

This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

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  1. Maia, N. M. M., Silva, J. M. M., He, J., Lieven, N. A. J., Lin, R. M., Skingle, G. W., To, W.-M., and Urgueira, A. P. V., 1997, Theoretical and Experimental Modal Analysis, Research Studies Press, Taunton, Somerset, UK.
  2. Ewins, D. J., 2000, Modal Testing: Theory, Practice and Application, Research Studies Press, Baldock, Hertfordshire, UK.
  3. Abdelghani, M., Verhaegen, M., Van Overschee, P., and De Moor, B., 1998, “Comparison study of subspace identification methods applied to flexible structures,” Mech. Syst. Signal Process., 12, No. 5, pp. 679–692. · doi:10.1006/mssp.1998.0169
  4. Petsounis, K. A., and Fassois, S. D., 2001, “Critical comparison and assessment of parametric time domain methods for the identification of vibrating structures.” Mech. Syst. Signal Process., accepted for publication. · doi:10.1006/mssp.2001.1424
  5. Peeters, B., 2000, “System Identification and Damage Detection in Civil Engineering,” PhD thesis, Department of Civil Engineering, Katholieke Universiteit Leuven, Belgium, [http://www.bwk.kuleuven.ac.be/bwm], Dec.
  6. Hermans, L., and Van der Auweraer, H., 1999, “Modal testing and analysis of structures under operational conditions: industrial applications,” Mech. Syst. Signal Process., 13, No. 2, pp. 193–216. · DOI · Paper page
  7. Peeters, B., and De Roeck, G., 2001, “One-year monitoring of the Z24-Bridge: environmental effects versus damage events,” Earthquake Eng. Struct. Dyn., 30, No. 2, 149–171. · DOI
  8. Ljung, L., 1999, System Identification: Theory for the User, Second Edition, Prentice-Hall, Upper Saddle River, NJ.
  9. Peeters, B., and De Roeck, G., 1999, “Reference-based stochastic subspace identification for output-only modal analysis,” Mech. Syst. Signal Process., 13, No. 6, pp. 855–878. · DOI · Paper page
  10. Akaike, H. , 1974, “Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes,” Annals of the Institute of Statistical Mathematics, 26, pp. 363–387. · doi:10.1007/bf02479833
  11. Caines, P., 1998, Linear Stochastic Systems, Wiley, New York.
  12. Bendat, J. S. and Piersol, A. G., 1993, Engineering Applications of Correlation and Spectral Analysis, Second Edition, Wiley, New York.
  13. Felber, A. J., 1993, “Development of a Hybrid Bridge Evaluation System,” PhD thesis, University of British Columbia, Vancouver, Canada.
  14. Cunha, A., Caetano, E., Calc¸ada, R., and Delgado, R., 1999, “Modal identification and correlation with finite element parameters of Vasco da Gama Bridge,” Proceedings of IMAC 17, pp. 705–711, Kissimmee, FL, Feb.
  15. Prevosto, M., 1982, “Algorithmes d’Identification des Caracte´ristiques Vibratoires de Structures Me´caniques Complexes,” PhD thesis, Universite´ de Rennes I, France, Mar.
  16. Shih, C. Y., Tsuei, Y. G., Allemang, R. J., and Brown, D. L., 1988, “Complex mode indication function and its application to spatial domain parameter estimation,” Mech. Syst. Signal Process., 2, No. 4, pp. 367–377. · doi:10.1016/0888-3270(88)90060-x
  17. Brincker, R., Zhang, L., and Andersen, P., 2000, “Modal identification from ambient responses using frequency domain decomposition,” Proceedings of IMAC 18, the International Modal Analysis Conference, pp. 625–630, San Antonio, TX, Feb.
  18. Golub, G. H. and Van Loan, C. F., 1996, Matrix Computations, Third Edition, The Johns Hopkins University Press, Baltimore, MD.
  19. Schoukens J. and Pintelon R., 1991, Identification of Linear Systems: a Practical Guideline to Accurate Modelling, Pergamon Press, London, UK.
  20. Pintelon, R., Guillaume, P., Rolain, Y., Schoukens, J., and Van Hamme, H., 1994, “Parametric identification of transfer functions in the frequency domain-a survey,” IEEE Trans. Autom. Control, AC-39, No. 11, pp. 2245–2260. · doi:10.1109/9.333769
  21. Guillaume P., Verboven P., and Vanlanduit S., 1998, “Frequency-domain maximum likelihood identification of modal parameters with confidence intervals,” Proceedings of ISMA 23, Noise and Vibration Engineering, K. U. Leuven, Belgium.
  22. Vanlanduit, S, Guillaume P., and Schoukens, J., 1998, “High spatial resolution modal parameter estimation using a parametric MLE-like algorithm,” Proceedings of ISMA 23, Noise and Vibration Engineering, K. U. Leuven, Belgium.
  23. Hermans, L., Guillaume, P., and Van der Auweraer, H., 1998, “A frequency-domain maximum likelihood approach for the extraction of modal parameters from output-only data,” Proceedings of ISMA 23, Noise and Vibration Engineering, K. U. Leuven, Belgium.
  24. James, G. H., Carne, T. G., and Lauffer, J. P., 1995, “The natural excitation technique (NExT) for modal parameter extraction from operating structures,” Int. J. Anal. Exp. Modal Anal., 10, No. 4, pp. 260–277.
  25. Leuridan, J., 1984, “Some Direct Parameter Model Identification Methods Applicable for Multiple Input Modal Analysis,” PhD thesis, University of Cincinnati, OH.
  26. Allemang, R. J., Brown, D. L., and Fladung, W. A., 1994, “Modal parameter estimation: a unified matrix polynomial approach,” Proceedings of IMAC 12, the International Modal Analysis Conference, pp. 501–514, Honolulu, HI.
  27. Benveniste, A., and Fuchs, J.-J., 1985, “Single sample modal identification of a nonstationary stochastic process,” IEEE Trans. Autom. Control, AC-30, No. 1, pp. 66–74. · DOI
  28. Ho, B. L., and Kalman, R. E., 1966, “Effective construction of linear state-variable models from input/output data,” Regelungstechnik, 14, pp. 545–548. · doi:10.1524/auto.1966.14.112.545
  29. Zeiger, H. P., and McEwen, A. J., 1974, “Approximate linear realization of given dimension via Ho’s algorithm,” IEEE Trans. Autom. Control, AC-19, No. 2, pp. 5353. · DOI
  30. Kung, S. Y., 1974, “A new identification and model reduction algorithm via singular value decomposition.” Procedings of the 12th Asilomar Conference on Circuits, Systems and Computers, pp. 705–714, Asilomar, CA, USA, Nov.
  31. Juang, J.-N., and Pappa, R. S., 1985, “An eigensystem realization algorithm for modal parameter identification and model reduction,” J. Guid. Control Dyn. · doi:10.2514/6.1986-2048
  32. Juang, J.-N., 1994, Applied System Identification, Prentice Hall, Englewood Cliffs, NJ.
  33. Desai, U. B., Pal, D., and Kirkpatrick, R. D., 1985, “A realization approach to stochastic model reduction,” Int. J. Control 42, pp. 821–839. · doi:10.1080/00207178508933398
  34. Aoki M., 1987, State Space Modelling of Time Series, Springer-Verlag, Berlin, Germany. · DOI
  35. Arun, K. S., and Kung, S. Y., 1990, “Balanced approximation of stochastic systems,” SIAM J. Matrix Anal. Appl., 11, pp. 42–68. · DOI
  36. Akaike, H. , 1974, “Stochastic theory of minimal realization,” IEEE Trans. Autom. Control, 19, pp. 667–674. · DOI · Paper page
  37. Van Overschee, P., and De Moor, B. 1996, Subspace Identification for Linear Systems: Theory-Implementation-Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands. · DOI · Paper page
  38. Camba-Mendez, G., and Kapetanios, G., 2001, “Testing the rank of the Hankel covariance matrix: a statistical approach,” IEEE Trans. Autom. Control, AC-46, No. 2, pp. 325–330. · doi:10.1109/9.905704
  39. Van Overschee, P., and De Moor, B., 1993, “Subspace algorithm for the stochastic identification problem,” Automatica, 29, No. 3, pp. 649–660. · DOI
  40. Viberg, M. , 1995, “Subspace-based methods for the identification of linear time-invariant systems,” Automatica, 31, No. 12, pp. 1835–1851. · DOI · Paper page
  41. Kirkegaard P. H., and Andersen, P., 1997, “State space identification of civil engineering structures from output measurements,” Proceedings of IMAC 15, The International Modal Analysis Conference, pp. 889–895, Orlando, FL.
  42. Peeters, B., De Roeck, G., and Andersen, P., 1999, “Stochastic system identification: uncertainty of the estimated modal parameters,” Proceedings of IMAC 17, The International Modal Analysis Conference, pp. 231–237, Kissimmee, FL, Feb.
  43. Piombo, B., Ciorcelli, E., Garibaldi, L., and Fasana, A., 1993, “Structures identification using ARMAV models,” Proceedings of IMAC 11, the International Modal Analysis Conference, pp. 588–592, Orlando, FL.
  44. Andersen, P., 1997, “Identification of Civil Engineering Structures Using Vector ARMA Models,” PhD thesis, Department of Building Technology and Structural Engineering, Aalborg University, Denmark, May.
  45. Pandit, S. M., 1991, Modal and Spectrum Analysis: Data Dependent Systems in State Space, Wiley, New York.
  46. De, Roeck G., Claesen, W., and Van den Broeck, P., 1995, “DDS-methodology applied to parameter identification of civil engineering structures,” Vibration and Noise ’95, pp. 341–353, Venice, Italy, Apr.

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Metadata

Authors
Peeters, B., & De Roeck, G.
Venue
Journal of Dynamic Systems, Measurement, and Control
Publisher
ASME International
Published
2001-02-07
Volume
123
Issue
4
Pages
659-667
ISSN
  • 0022-0434 (ISSN-L)
  • 1528-9028
DOI
10.1115/1.1410370
OpenAlex
W2166134217
Cited by
996
References
0
FWCI
24.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stochastic System Identification for Operational Modal Analysis: A Review. https://app.cochat.ai/papers/10.1115/1.1410370
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Peeters, B., & De Roeck, G. (2001). Stochastic System Identification for Operational Modal Analysis: A Review. Journal of Dynamic Systems, Measurement, and Control. https://doi.org/10.1115/1.1410370

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