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.
KeywordsStructural health monitoring Damage detection Reduced-order modeling Proper orthogonal decomposition Extended Kalman-particle filtering Thin plates Finite element
The research by S.E.A. has been implemented under the “ARISTEIA” Action of the “Operational Programme Education and Lifelong Learning” and was co-funded by the European Social Fund (ESF) and Greek National Resources. G.C. and S.M. wish to acknowledge a financial support from Fondazione Cariplo through project “Safer Helmets”.
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