The use of reduced finite element models in system identification

Abstract

AbstractAn a priori, analytical model in system identification of vibrating structures is improved by input and output measurements with least square fitting. The structure is modelled by the finite element method. Finite element models usually need a large number of degrees of freedom to simulate a small number of lower spectrum eigenfrequencies with accuracy. The large finite element model is reduced to a subspace of significant eigenfrequencies. The size of the subspace is chosen with regard to the frequency content of the measured data and the accuracy of the large analytical model. An identification method is formulated for the large analytical model. This procedure improves system parameters in the matrices of the large model by measured input and output data with a least square functional. The objective function is consistently reduced, so that the whole identification procedure can be performed in the small subspace. The proposed reduction method permits very large and accurate analytical models to be used, and it decreases the computational cost of the identification procedure significantly. The computational efficiency is demonstrated on an in situ experiment of a radar tower.