Uncertainty Identification by the Maximum Likelihood Method

JR Fonseca (University of Wales Swansea), MI Friswell (University of Bristol), JE Mottershead (Liverpool University) & AW Lees (University of Wales Swansea)

Special Issue of the Journal of Sound and Vibration on Uncertainty, Vol. 288, No. 3, December 2005, pp. 587-599


To incorporate uncertainty in structural analysis knowledge of the uncertainty in the model parameters is required. This paper describes efficient techniques to identify and quantify variability in the parameters from experimental data by maximising the likelihood of the measurements, using the well-established Monte Carlo or perturbation methods for the likelihood computation. These techniques are validated numerically and experimentally on a cantilever beam with a point mass at an uncertain location. Results show that sufficient accuracy is attainable without a prohibitive computational effort. The perturbation approach requires less compuation but is less accurate when the response is a highly nonlinear function of the parameters.

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This material has been published in the Journal of Sound and Vibration, Vol. 288, No. 3, December 2005, pp. 587-599, the only definitive repository of the content that has been certified and accepted after peer review. Copyright and all rights therein are retained by the Elsevier.

Link to paper using doi:10.1016/j.jsv.2005.07.006

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