Stochastic model updating—Covariance matrix adjustment from uncertain experimental modal data

Abstract

With deterministic methods finite element model parameters are updated by using a single set of experimental data. As a consequence the corrected analytical model only reflects this single test case. However, test data are inherently exposed to uncertainty due to measurement errors, different modal extraction techniques, etc. Even a more relevant factor for variability originates from production tolerances and consequently the question arises, how to describe model parameters from the stochastic point of view? Therefore it would be desirable to use statistical properties of multiple sets of experimental and to consider the update parameters as random variables. This paper presents an inverse approach how to identify a stochastic finite element model from uncertain test data. In detail, this work demonstrates a method to adjust design parameter means and their related covariance matrix from multiple sets of experimental modal data. Results are shown from a numerical example.