Integrated Airframe Digital Twin Framework for Fatigue Tracking of Unmanned Aerial Vehicles

Structural fatigue is one of the primary factors affecting the structural integrity and operational safety of aircraft during their service life. As flight hours accumulate and mission profiles become increasingly complex, accurately assessing and predicting fatigue damage becomes critical for ensuring airworthiness and optimizing maintenance planning. The Airframe Digital Twin has emerged as a promising paradigm for addressing this challenge, enabling structural fatigue diagnosis and prognosis through the development of multiphysics, multiscale, and probabilistic virtual representations of as-built systems. This paper presents a comprehensive and integrated framework for constructing the digital twin of an unmanned aerial vehicle, incorporating in-service load tracking, multilevel structural analysis, and probabilistic diagnosis and prognosis. The flight test of the unmanned aerial vehicle is utilized to validate the proposed framework. Results demonstrate that the developed digital twin can effectively predict fatigue crack growth in real time using only flight parameters as input. Furthermore, with inspection data available, the digital twin can be updated to provide a more accurate prediction of future damage evolution. These insights offer valuable guidance to optimize aircraft fleet maintenance strategies, thereby enhancing safety and cost-effectiveness.