Modal Parameter Estimation from Input-Output Fourier Data using Frequency-domain Maximum Likelihood Identification

Peter Verboven, Bart Cauberghe, Eli Parloo, Steve Vanlanduit, Patrick Guillaume

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

A multi-variable frequency-domain maximum likelihood estimator is
proposed to identify the modal parameters together with confidence
intervals directly from the input-output Fourier data. The use of
periodic excitation signals enables the use of a so-called
non-parametric errors-in-variables noise model for an accurate
description of the measurement set-up. The combination with a maximum
likelihood identification approach yields a solver that is extremely
robust to errors in the data, such as noise and leakage and hence
results in accurate models. Since the maximum likelihood approach
involves an optimization problem, a least-squares estimator is proposed
as well, with the availability of a stabilization diagram. Both
algorithms have been optimized for modal analysis applications by a
significant reduction of the computation time and memory requirements.
In the case when random noise excitation is required, the proposed
method allows a parametric compensation for effects of leakage. (C)
2003 Elsevier Ltd. All rights reserved.
Original languageEnglish
JournalJournal of sound and vibration
Publication statusPublished - 2004

Bibliographical note

Journal of Sound and Vibration, vol.276, pp.957-979

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