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Title
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Modal parameter estimation from input-output Fourier data using frequency-domain maximum likelihood identification
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of sound and vibration / University of Southampton. Institute of Sound and Vibration Research. - London
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Publication
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London
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2004
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ISSN
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0022-460X
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DOI
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10.1016/J.JSV.2003.08.044
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Volume/pages
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276
:3-5
(2004)
, p. 957-979
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ISI
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000223528000024
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Full text (Publisher's DOI)
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