Maximum Likelihood Identification of Modal Parameters from Operational Data
نویسنده
چکیده
In the present contribution, the applicability of frequencydomain Maximum Likelihood (ML) identification techniques in the field of operational modal analysis is investigated. Attention is paid to the derivation of the spectral densities and noise information required for the ML estimator. The frequency-domain ML approach is applied to a real case study and confidence intervals are derived on the estimated parameters. Using these confidence intervals, it is possible to automate the model orde selection.
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