Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering
AbstractHealth monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated. View Full-Text
Share & Cite This Article
Capellari, G.; Eftekhar Azam, S.; Mariani, S. Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering. Sensors 2016, 16, 2.
Capellari G, Eftekhar Azam S, Mariani S. Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering. Sensors. 2016; 16(1):2.Chicago/Turabian Style
Capellari, Giovanni; Eftekhar Azam, Saeed; Mariani, Stefano. 2016. "Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering." Sensors 16, no. 1: 2.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.