Hybrid reduced-order modeling and particle-Kalman filtering for the health monitoring of flexible structures

MEMS-based, surface-mounted structural health monitoring systems were recently proposed to locate possible damage events in lightweight composite structures. To track the structural dynamics induced by the external actions and identify in real-time the inception of drifts from the virgin, or undamaged state, recursive Bayesian filters are here adopted. As the main drawback of any on-line identification method might be linked to the excessive computational costs, two solutions are jointly enforced: an order-reduction of the numerical model used to track the structural behavior, through the proper orthogonal decomposition in its snapshot-based version; an improved particle filtering strategy, which features an extended Kalman updating of each evolving particle before the resampling stage. While the former method alone can reduce the number of effective degrees-of-freedom of the structure to a few only (depending on the excitation), the latter allows to track the evolution of damage and also locate it thanks to an intricate formulation. To assess the proposed procedure, the case of a thin plate subject to bending is investigated; it is shown that, when the procedure is fed by measurements gathered by a network of inertial MEMS sensors appropriately deployed over the plate, damage is efficiently and accurately estimated and located.