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.

Structural assessment and sensor placement strategy for self-aware aerospace vehicles / Mainini, Laura. - ELETTRONICO. - 1:(2017), pp. 1586-1594. ((Intervento presentato al convegno 11th International Workshop on Structural Health Monitoring 2017: IWSHM 2017 tenutosi a Stanford University, usa nel 2017 [10.12783/shm2017/14035].

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

Mainini, Laura
2017

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2694508