Hierarchical local model trees for design of experiments in the framework of ultrasonic structural health monitoring

In this paper, we propose an effective and time-saving algorithm for model-based design of experiments in the framework of a structural health monitoring system. The goal is to identify and locate structural defects in plate-like geometries. The new idea combines a pseudo-random Monte-Carlo sampling with a local model network. The global distribution of data points is based on the input space partitioning which can be seen as a mapping of the non-linearities of the underlying process. This results in an active learning strategy that incorporates the process behavior into the experimental design strategy. The application of the proposed algorithm for ultrasonic imaging in an isotropic non-convex structure shows great potential. It is shown that in contrast to a grid-based approach the spatial discretization can be optimized with high accuracy and adaptivity.