Online damage detection in plates via vibration measurements

In this work, we propose a new framework for the online detection of damage in plates via vibration measurements. To this end, a finite element model of the plate is handled by a recursive Bayesian filter for simultaneous state and parameter estimation. To drastically reduce the computational costs and enhance the robustness of the filter, such model is projected onto a (sub-)space spanned by a few vibration modes only, which are provided by a snapshot-based proper orthogonal decomposition (POD) method. A challenge in using such approach for damaging structures stems from the fact that vibration modes can be adjusted only during the training stage of the analysis; if damage occurs or grows when the reduced-order model is at work, the training stage has to be re-started. Here, an alternate method is proposed to concurrently update the sub-space spanned by the modes and to provide estimates of damage location and amplitude. The robustness and accuracy of the proposed approach are ascertained through an ad-hoc pseudo-experimental campaign.