The Future of Aircraft Maintenance: Goals and Challenges of Digital Twins for In-flight Operations

The aviation sector is rapidly advancing towards the next generation of aircraft, expected by 2035, propelled by Artificial Intelligence (AI), cloud computing, and cybersecurity technologies. The Digital Twin is at the forefront of these advancements as it aims to integrate all these technologies. However, significant challenges arise in implementing Digital Twins, particularly for in-service aircraft with limited computational resources. This research aims to develop a power-efficient Digital Twin framework tailored for predictive maintenance. After an extensive review of the latest Digital Twin research, considerable effort was focused on creating a high-fidelity model of a critical aircraft component. Based on this model, a methodology for fault data simulation has been developed. The simulated data, generated through fault injection in a multi-physics model, will be the foundation for designing an effective machine-learning algorithm for predictive maintenance. Finally, the algorithm will be deployed on a device with limited computational resources without compromising the system’s reliability.