A hybrid approach for Structural Monitoring with self-organizing multi-agent systems and inverse numerical methods in material-embedded sensor networks

One of the major challenges in Structural Monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated structural health and load monitoring systems by using self-organizing mobile multi-agent systems (MAS), and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure with low-resource agent processing platforms scalable to microchip level, enabling material-integrated real-time sensor systems. The MAS is deployed in a heterogeneous environment and offers event-based sensor preprocessing, distribution, and pre-computation. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, the computation is partitioned into spatial off-line (outside the network) and on-line parts, with on-line sensor processing performed by the agent system. A unified multi-domain simulation framework is used to profile and validate the proposed approach.