Publication
Title
Modal parameter estimation from input-output Fourier data using frequency-domain maximum likelihood identification
Author
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.
Language
English
Source (journal)
Journal of sound and vibration / University of Southampton. Institute of Sound and Vibration Research. - London
Publication
London : 2004
ISSN
0022-460X
Volume/pages
276:3-5(2004), p. 957-979
ISI
000223528000024
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 04.11.2014
Last edited 21.09.2017