Test Data Informativeness Assessment for Finite Element Model Updating

• Before a computational model updating or an error localization is to be carried out, a prepara­ tory error localization using only analytical data is justified. The purpose of such preparation should be to select the parameters for quantifying model errors and also to design optimal tests for determining the correct parameter setting. For a successful error localization, it is required that the test data should be informative with respect to the parameters chosen. The demand for test data informativeness limits the experiment with regard to the spatial resolution of sensors, bandwidth of excitation, signal-to-noise ratios, etc. On the other hand, for a given test condition and test data, the omnipresent noise may make parameter estimates useless because of estima­ tion covariances that are too large. This is often caused by over parameterized models; these should be identified by the preparatory error localization and remedied by are-parameterization before model updating take place. The aim of this study is to quantify data informativeness with respect to physical parameters that are used in error localization and model updating. The data informativeness  is shown to relate to the Fisher information matrix. Deterministic finite-element related state-space models in combination with stochastic noise models are used for evaluating data informativeness.  A nu­ merical study utilizing a finite-element model validated by test data from a scanning laser vi­ brometer is used to substantiate the theory.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Teknisk mekanik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Applied Mechanics (hsv//eng)

Keyword

Informativeness Test Finite Element Model Updating

Maskinteknik

Mechanical Engineering

Publication and Content Type

vet (subject category)

rap (subject category)