Confidence Intervals of Modal Parameters during Progressive Damage Test

Linking Models and Experiments, Volume 2 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-1-4419-9305-2_17 ◽

2011 ◽

pp. 237-250 ◽

Cited By ~ 2

Author(s):

Michael Döhler ◽

Falk Hille ◽

Xuan-Binh Lam ◽

Laurent Mevel ◽

Werner Rücker

Keyword(s):

Confidence Intervals ◽

Modal Parameters ◽

Progressive Damage

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Estimation of Modal Parameters Confidence Intervals: A Simple Numerical Example

Special Topics in Structural Dynamics, Volume 6 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-1-4614-6546-1_65 ◽

2013 ◽

pp. 611-620

Author(s):

Elisa Bosco ◽

Ankit Chiplunkar ◽

Joseph Morlier

Keyword(s):

Confidence Intervals ◽

Modal Parameters ◽

Numerical Example

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A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model

Applied Sciences ◽

10.3390/app9153120 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3120

Author(s):

Sandro Amador ◽

Mahmoud El-Kafafy ◽

Álvaro Cunha ◽

Rune Brincker

Keyword(s):

Maximum Likelihood ◽

Least Squares ◽

Maximum Likelihood Estimator ◽

Confidence Intervals ◽

Modal Parameters ◽

Parameter Estimates ◽

Modal Parameter ◽

Likelihood Estimator ◽

Pole Residue ◽

Modal Model

Recently, a lot of efforts have been devoted to developing more precise Modal Parameter Estimation (MPE) techniques. This is explained by the necessity in civil, mechanical and aerospace engineering of obtaining accurate estimates for the modal parameters of the tested structures, as well as of determining reliable confidence intervals for these estimates. The Non-linear Least Squares (NLS) identification techniques based on Maximum Likelihood (ML) have been increasingly used in modal analysis to improve precision of estimates provided by the Least Squares (LS) based estimators when they are not accurate enough. Apart from providing more accurate estimates, the main advantage of the ML estimators, with regard to their LS counterparts, is that they allow for taking into account not only the measured Frequency Response Functions (FRFs) but also the noise information during the parametric identification process and, therefore, provide the modal parameters estimates together with their uncertainties bounds. In this paper, a new derivation of a Maximum Likelihood Estimator formulated in Pole-residue Modal Model (MLE-PMM) is presented. The proposed formulation is meant to be used in combination with the Least Squares Frequency Domain (LSCF) to improve the precision of the modal parameter estimates and compute their confidence intervals. Aiming at demonstrating the efficiency of the proposed approach, it is applied to two simulated examples in the final part of the paper.

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