A Bayesian probabilistic approach for damage identification in plate structures using responses at vibration nodes

Abstract

Structural health monitoring for plate structures is important since they are essential structural components in many applications. One interesting topic for structural health monitoring methods is to achieve accurate damage detection with a small number of sensors and without the requirement of a high-fidelity finite element model. Traditional damage detection techniques for plates need many sensors to be distributed on the surface of the plate. This paper adopts dynamic responses at a few vibration nodal points combined with a Bayesian probabilistic approach for damage identification in plate structures. Vibrational amplitudes at nodal points, also referred as node displacement or NODIS, have the potential to achieve real-time damage assessment with a relatively small number of sensors. Thus, they can serve as efficient structural damage indicators. Despite these advantages, this method has not been applied to plate-type structures. This paper proposes a vibration-based SHM method for plates that is suitable for real-time monitoring, requires a small number of industrial sensors, does not rely on a high-fidelity FE model and can be applied for damage assessment of location and severity. In the Bayesian framework, an efficient perturbation-based surrogate model is derived for plate structures to replace the expensive FE model. The accuracy of the perturbation-based surrogate model is investigated and compared with FE results. Then, this paper evaluates the performance of the NODIS-based Bayesian framework with the perturbation method by comparing it with FE results. At last, the proposed method is applied to a carbon fiber reinforced polymer sandwich structure with different grinding depths.