A Fast Maximum Likelihood-Based Estimation of a Modal Model

El-Kafafy, Mahmoud; Accardo, Giampiero; Peeters, Bart; Janssens, Karl; Troyer, Tim De; Guillaume, Patrick

doi:10.1007/978-3-319-15251-6_15

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…The MLMM modal parameter estimation method is a multivariable (i.e., MIMO) frequencydomain method that uses the modal model as a parametric model to represent the measured FRFs over a chosen frequency band. The MLMM method is originally introduced in [1] and further improved in terms of the computational load in [2]. In [3], the MLMM method is adapted to consider some desired physically motivated constraints (e.g., FRFs reciprocity) in the optimization process.…”

Section: Mlmm: Basic Backgroundmentioning

confidence: 99%

A Robust Test-Based Modal Model Identification Method for Challenging Industrial Cases

El-Kafafy

Peeters

Geluk

et al. 2018

The 18th International Conference on Experimental Mechanics

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In this paper, the MLMM modal parameter estimation method (Maximum Likelihood estimation of a Modal Model) and its new variant will be introduced. The MLMM method tackles some of the remaining challenges in modal analysis (e.g., modal analysis of highly-damped cases where a large amount of excitation locations is needed such as the modal analysis of a trimmed car body). Another big advantage of the MLMM method is its capability to fully integrate, within the estimated modal model, some important physical constraints, which are required for the intended applications, e.g., realness of the mode shape and FRFs reciprocity. More classical modal parameter estimation methods have rarely the possibility to fully integrate these constraints and the obtained modal parameters are typically altered in a subsequent step to satisfy the desired constraints. It is obvious that this may lead to sub-optimal results. The MLMM method uses the Levenberg-Marquardt optimization scheme to directly fit the modal model to the measured FRFs. The applicability of MLMM to estimate an accurate constrained modal model will be demonstrated using two challenging industrial applications.

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Section: Mlmm: Basic Backgroundmentioning

confidence: 99%

A Robust Test-Based Modal Model Identification Method for Challenging Industrial Cases

El-Kafafy

Peeters

Geluk

et al. 2018

The 18th International Conference on Experimental Mechanics

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“…Recently, a modal parameter identification method called maximum likelihood modal model-based (ML-MM) has been introduced [23][24][25][26][27][28]. The basic implementation of the method is introduced in [24][25][26][27], while the computational speed of the method is significantly optimized in [23,28]. In the ML-MM method, the modal parameters are identified by fitting directly the modal model (see Eq.…”

Section: Constrained Modal Parameter Estimation : a Reviewmentioning

confidence: 99%

Constrained maximum likelihood modal parameter identification applied to structural dynamics

El-Kafafy

Peeters

Guillaume

et al. 2016

Mechanical Systems and Signal Processing

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A new modal parameter estimation method to directly establish modal models of structural dynamic systems satisfying two physically motivated constraints will be presented. The constraints imposed in the identified modal model are the reciprocity of the frequency response functions (FRFs) and the estimation of normal (real) modes. The motivation behind the first constraint (i.e. reciprocity) comes from the fact that modal analysis theory shows that the FRF matrix and therefore the residue matrices are symmetric for non-gyroscopic, non-circulatory, and passive mechanical systems. In other words, such types of systems are expected to obey Maxwell-Betti's reciprocity principle. The second constraint (i.e. real mode shapes) is motivated by the fact that analytical models of structures are assumed to either be undamped or proportional damped. Therefore, normal (real) modes are needed for comparison with these analytical models. The work done in this paper is a further development of a recently introduced modal parameter identification method called ML-MM that enables us to establish modal model that satisfies such motivated constraints. The proposed constrained ML-MM method is applied to two real experimental datasets measured on fully trimmed cars. This type of data is still considered as a significant challenge in modal analysis. The results clearly demonstrate the applicability of the method to real structures with significant non-proportional damping and high modal densities.

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“…As noted previously, 1,13,17 it was quite challenging for classical modal parameter estimation methods to curve-fit an FRF matrix with so many columns (12 references); typically, not all references are well fitted for a particular sensor location. Also, a very high model order needs to be selected to have a reasonable number of lines in the stabilization diagram (Figure 25), and the diagram itself is a bit less clear.…”

Section: Experimental Modal Analysismentioning

confidence: 99%

“…Maximum Likelihood Estimation Based on the Modal Model. The so-called ML-MM method 4,5,17 is a multiple-input, multipleoutput (MIMO) frequency-domain estimator providing global estimates of the modal model parameters. In the first step of the ML-MM estimator, initial values of all the modal parameters (poles, mode shapes, participation factors, and upper and lower residuals) have to be specified.…”

Section: Experimental Modal Analysismentioning

confidence: 99%