Parameter selection and stochastic model updating using perturbation methods with parameter weighting matrix assignment

The paper presents a method for model updating, called Quadratic Compression Method (QCM). The updated model has a fixed structure with some free parameters. Algebraic manipulations of the eigenvalue equation lead to a simplified equation with a lower dimension. This equation is then solved in a Least Squares sense. The method is shown to belong to the class of Minimization of the Error in the Characteristic Equation (MECE), with a particular choice of the weighting matrix. The paper presents also a weighted version of the method, called WQCM, which is motivated by reducing the effect of measuring noise. In addition to the theoretic analysis, the superior robustness to noise properties of QCM and WQCM are demonstrated by simulations and experimentally.

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Stochastic model updating for assembled structures with bolted joints using a Bayesian method

Engineering Optimization ◽

10.1080/0305215x.2021.1965136 ◽

2021 ◽

pp. 1-19

Author(s):

Yong Zhang ◽

Yan Zhao ◽

Huajiang Ouyang

Keyword(s):

Stochastic Model ◽

Bayesian Method ◽

Model Updating ◽

Bolted Joints

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Parameter selection for model updating based on the global sensitivity method

Journal of Physics Conference Series ◽

10.1088/1742-6596/1106/1/012004 ◽

2018 ◽

Vol 1106 ◽

pp. 012004

Author(s):

Zhaoxu Yuan ◽

Kaiping Yu ◽

John E Mottershead

Keyword(s):

Model Updating ◽

Parameter Selection ◽

Global Sensitivity ◽

Selection For ◽

Sensitivity Method

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How Can Spatially Distributed Uncertainties Be Included in FEA and in Parameter Estimation for Model Updating?

Shock and Vibration ◽

10.1155/2003/594714 ◽

2003 ◽

Vol 10 (1) ◽

pp. 15-25 ◽

Cited By ~ 4

Author(s):

M.W. Zehn ◽

A. Saitov

Keyword(s):

Parameter Estimation ◽

Model Updating ◽

A Priori ◽

Estimation Methods ◽

Parameter Distribution ◽

Fe Model ◽

Weighting Matrix ◽

Spatially Distributed ◽

Updating Procedure ◽

Parameter Estimation Methods

Owing to manufacturing composite materials and others show considerable uncertainties in wall-thickness, fluctuations in material properties and other parameter, which are spatially distributed over the structure. These uncertainties have a random character and can therefore not being reduced by some kind of mesh refinement within the FE model. What we need is a suitable statistical approach to describe the parameter changing that holds for the statistics of the process and the correlation between the parameter spatially distributed over the structure. The paper presents a solution for a spatial correlated simulation of parameter distribution owing to the manufacturing process or other causes that is suitable to be included in the FEA. The parameter estimation methods used in updating algorithms for FE-models, depend on the choice of a priori to be determined weighting matrices. The weighting matrices are in most cases assumed by engineering judgement of the analyst carrying out the updating procedure and his assessment of uncertainty of parameters chosen and measured and calculated results. With the statistical description of the spatial distribution at hand, we can calculate a parameter weighting matrix for a Baysian estimator. Furthermore, it can be shown in principle that with model updating it is possible to improve the probabilistic parameter distribution itself.

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