Identification of structural dynamic parameters from vision-based measurements

In this work we present a novel approach for the time-domain system identification of structural dynamic components exploiting the high spatial density of vision-based measurements. By projecting the spatially dense, time-domain measurements on a low-order dominant deformation motion basis, relatively noise-free, low-order dynamic states could directly be extracted and used in an optimization procedure for the identification of the structural dynamic parameters. Here, a priori physical knowledge of the underlying set of partial differential equations is used to determine the structure of the model to identify. The presented approach is experimentally validated on a clamped plate as well as on a flexibly suspended plate which required a correction of the rigid body motion. The results of this work have shown to be highly accuracy up to 1e-5 meter with respect to the dominant measured motion components in both validation cases and encourage us to rethink other experimental testing techniques towards the use of vision-based measurements.

ISBN:9789082893151

Jaar van publicatie:2022

Toegankelijkheid:Open