Uncertainty identification by the maximum likelihood method

JR Fonseca, MI Friswell, JE Mottershead, AW Lees

Research output: Contribution to journalArticle (Academic Journal)peer-review

113 Citations (Scopus)


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.
Translated title of the contributionUncertainty identification by the maximum likelihood method
Original languageEnglish
Pages (from-to)587 - 599
Number of pages13
JournalJournal of Sound and Vibration
Volume288 (3)
Publication statusPublished - Dec 2005

Bibliographical note

Publisher: Elsevier Ltd


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