Structural assessment and sensor placement strategy for self-aware aerospace vehicles

This paper addresses real-time structural health assessment as a form of aircraft selfawareness. Limited time and resources available on-board and incomplete measured data affected by uncertainty are the key challenges to face. We discuss a data-driven methodology that combines Multi-Step Reduced Order Modeling to support structural self-awareness, and unsupervised learning (Self-Organizing Maps) to identify optimal sets of sensor locations. In particular, two implementations of our sensor placement strategy are presented and compared for a composite wing panel subjected to a number of damage conditions.