Digital Twins for Military Aircraft: a Machine Learning Approach for Monitoring Structural Aging
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Unlike civil aviation, where regular maintenance schedules are feasible, military aircraft are subjected to highly variable flight conditions based on mission requirements. This makes real-time assessment of structural fatigue critical for safety. Traditional approaches rely on physics-based models to predict mechanical strains, fatigue, and aging from input flight control data. While these models provide significant insights, they may not fully capture the complexities of real-world conditions and can benefit from refinement. Our research seeks to enhance these models by using machine learning to create digital twins of strain gauges for military aircraft, capable of predicting structural strains and aging based on input-output flight data. A part of these flight data, such as altitude, Mach number or others, are routinely measured during flight missions. In this work, structural strains are additionally recorded at critical points on one instrumented aircraft using strain gauges. The objective is then to develop a robust machine learning framework that simulates the behavior of critical aircraft strain gauges under varying operational conditions. In other words, using flight parameters as inputs, we aim to predict the strains experienced at specific points on the aircraft. This predictive capability can improve the planning of maintenance activities, guaranteeing maintenance in operational condition, and therefore enhancing flight safety.
