Robust model updating with insufficient data

Abstract

The increasing need of many industrial fields for highly accurate predictions of performance and reliability gives rise to the need for enhanced underlying mathematical models. The thereby available test data are usually rather limited due to the high costs of experimental measurements. Therefore, decisions have to be made based on limited, incomplete information, which poses a challenging problem. Recently, an approach for coping with insufficient data has been introduced that attempts to extract the information delivered by the data and processes it using few additional assumptions. The underlying distribution is based on an appropriate confidence level providing a safeguard against severe underestimation of the variability of the measured quantities. This method has been applied within the field of statics involving the stochastic identification of one single structural parameter. The present paper shows the extension of this approach to the field of dynamics. It is shown how to deal with insufficient information by applying kernel densities on the stochastic representation of modal data. In addition, the problem of correlation of the established multi-dimensional probability density function will be addressed. As a numerical example the structural dynamics application of the Validation Challenge Workshop has been chosen.