Skip to main content

Operational Modal Analysis in Frequency Domain Using Gaussian Mixture Models

  • Conference paper
  • First Online:
Topics in Modal Analysis & Testing, Volume 10

Abstract

Operational Modal Analysis is widely gaining popularity as a means to perform system identification of a structure. Instead of using a detailed experimental setup Operational Modal Analysis relies on measurement of ambient displacements to identify the system. Due to the random nature of ambient excitations and their output responses, various statistical methods have been developed throughout the literature both in the time-domain and the frequency-domain. The most popular of these algorithms rely on the assumption that the structure can be modelled as a multi degree of freedom second order differential system. In this paper we drop the second order differential assumption and treat the identification problem as a curve-fitting problem, by fitting a Gaussian Mixture Model in the frequency domain. We further derive equivalent models for the covariance-driven and the data-driven algorithms. Moreover, we introduce a model comparison criterion to automatically choose the optimum number of Gaussian’s. Later the algorithm is used to predict modal frequencies on a simulated problem.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Guillaume, P., Verboven, P., Vanlanduit, S., Van Der Auweraer, H., Peeters, B.: A poly-reference implementation of the least-squares complex frequency-domain estimator. Proc. IMAC 21, 183–192 (2003)

    Google Scholar 

  2. Richardson, M.H., Formenti, D.L.: Parameter estimation from frequency response measurements using rational fraction polynomials. Proceedings of the 1st International Modal Analysis Conference, vol. 1, pp. 167–186. Union College, Schenectady, NY (1982)

    Google Scholar 

  3. Peeters, B., Van der Auweraer, H., Pauwels, S., Debille, J.: Industrial relevance of Operational Modal Analysis–civil, aerospace and automotive case histories. In: Proceedings of IOMAC, the 1st International Operational Modal Analysis Conference (2005)

    Google Scholar 

  4. Shahdin, A., Morlier, J., Niemann, H., Gourinat, Y.: Correlating low energy impact damage with changes in modal parameters: diagnosis tools and FE validation. Struct. Health Monit. 10 (2), 199–217, (2010). http://journals.sagepub.com/doi/abs/10.1177/1475921710373297

    Article  Google Scholar 

  5. Rainieri, C., Fabbrocino, G., Cosenza, E.: Automated Operational Modal Analysis as structural health monitoring tool: theoretical and applicative aspects. Key Engineering Materials, vol. 347, pp. 479–484, Trans Tech Publ. (2007)

    Google Scholar 

  6. Lindsay, B.G., et al.: The geometry of mixture likelihoods: a general theory. Ann. Stat. 11 (1), 86–94 (1983)

    Google Scholar 

  7. McLachlan, G., Peel, D.: General introduction. In: Finite Mixture Models. Wiley, Hoboken (2000)

    Book  Google Scholar 

  8. Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. In: Shavlik, J.W., Dietterich, T.G. (eds.) Readings in Machine Learning, pp. 201–204. Morgan Kaufmann, San Francisco (1990). doi:10.1002/0471721182.ch1

    Google Scholar 

  9. Schwarz, G., et al.: Estimating the dimension of a model. Ann. Stat. 6 (2), 461–464 (1978)

    Google Scholar 

  10. Roeder, K., Wasserman, L.: Practical Bayesian density estimation using mixtures of normals. J. Am. Stat. Assoc. 92 (439), 894–902 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  11. Ljung, L.: System identification. In: Signal Analysis and Prediction, pp. 163–173. Springer, Birkhäuser/Boston (1998)

    Google Scholar 

  12. Andersen, P.: Identification of civil engineering structures using vector ARMA models. Ph.D. thesis, unknown (1997)

    Google Scholar 

  13. James III, O., Came, T.: The Natural Excitation Technique (NExT) for odal Paranieter extraction from operating structures (1995)

    Google Scholar 

  14. Spitznogle, F.R., Quazi, A.H.: Representation and analysis of time-limited signals using a complex exponential algorithm. J. Acous. Soc. Am. 47 (5A), 1150–1155 (1970)

    Article  Google Scholar 

  15. Ibrahim, S., Mikulcik, E.: A method for the direct identification of vibration parameters from the free response (1977)

    Google Scholar 

  16. Bochner, S.: Lectures on Fourier Integrals (AM-42), vol. 42. Princeton University Press, Princeton (2016)

    Google Scholar 

  17. Gade, S., Møller, N., Herlufsen, H., Konstantin-Hansen, H.: Frequency domain techniques for operational modal analysis. In: 1st IOMAC Conference (2005)

    Google Scholar 

  18. Zhang, L.: An overview of major developments and issues in modal identification, Proc. IMAC XXII, Detroit, pp. 1–8 (2004)

    Google Scholar 

  19. Brincker, R., Zhang, L., Andersen, P.: Modal identification from ambient responses using frequency domain decomposition. In: Proceedings of the 18th International Modal Analysis Conference (IMAC), San Antonio, TX (2000)

    Google Scholar 

  20. Brincker, R., Ventura, C., Andersen, P.: Damping estimation by frequency domain decomposition. In: 19th International Modal Analysis Conference, pp. 698–703 (2001)

    Google Scholar 

  21. Allemang, R.J., Brown, D.: A unified matrix polynomial approach to modal identification. J. Sound Vib. 211 (3), 301–322 (1998)

    Article  MATH  Google Scholar 

  22. Chauhan, S., Martell, R., Allemang, R., Brown, D.: Unified matrix polynomial approach for operational modal analysis. In: Proceedings of the 25th IMAC, Orlando, FL (2007)

    Google Scholar 

  23. Plataniotis, K.N., Hatzinakos, D.: Advanced Signal Processing Handbook Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems. CRC, Boca Raton (2000) 

    Google Scholar 

  24. Stuttle, M.N.: A Gaussian mixture model spectral representation for speech recognition. Ph.D. thesis, University of Cambridge (2003)

    Google Scholar 

  25. Xu, L., Jordan, M.I.: On convergence properties of the EM algorithm for Gaussian mixtures. Neural Comput. 8 (1), 129–151 (1996)

    Article  Google Scholar 

  26. Bishop, C.M.: Pattern recognition. Machine Learning, vol. 128. Springer, New York (2006)

    Google Scholar 

  27. Wilson, A.G., Adams, R.P.: Gaussian process kernels for pattern discovery and extrapolation supplementary material and code (2013). http://mlg.eng.cam.ac.uk/andrew/smkernelsupp.pdf

    Google Scholar 

  28. Wilson, A.G., Adams, R.P.: Gaussian process Kernels for pattern discovery and extrapolation. In: ICML (3), pp. 1067–1075 (2013)

    Google Scholar 

  29. Rasmussen, C.E.: Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning, pp. 63–71. Springer, Berlin/Heidelberg (2004)

    Google Scholar 

  30. Williams, R., Crowley, J., Vold, H.: The multivariate mode indicator function in modal analysis. In: International Modal Analysis Conference, pp. 66–70 (1985)

    Google Scholar 

  31. Shih, C., Tsuei, Y., Allemang, R., Brown, D.: Complex mode indication function and its applications to spatial domain parameter estimation. Mech. Syst. Signal Process. 2 (4), 367–377 (1988)

    Article  MATH  Google Scholar 

  32. Findley, D.F.: Counterexamples to parsimony and BIC. Ann. Inst. Stat. Math. 43 (3), 505–514 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  33. Leroux, B.G., et al.: Consistent estimation of a mixing distribution. Ann. Stat. 20 (3), 1350–1360 (1992)

    Google Scholar 

  34. MathWorks, I.: Curve Fitting Toolbox 1: User’s Guide. MathWorks, Natick (2006)

    Google Scholar 

Download references

Acknowledgements

The author’s are really indebted to the encouragement and support provided by, Jonatan Santiago Tonato, Emmanuel Rachelson, Michele Colombo and Sebastien Blanc.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 The Society for Experimental Mechanics, Inc.

About this paper

Cite this paper

Chiplunkar, A., Morlier, J. (2017). Operational Modal Analysis in Frequency Domain Using Gaussian Mixture Models. In: Mains, M., Blough, J. (eds) Topics in Modal Analysis & Testing, Volume 10. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-54810-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54810-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54809-8

  • Online ISBN: 978-3-319-54810-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics