Digital Twin-Based Health Management for Complex Aircraft Systems: Case Studies and Applications

Digital Twin technology, initially conceptualized during the NASA's Apollo program, has evolved into a transformative tool for system health management, particularly in aviation. By integrating high-fidelity simulations, real-time sensor data, and predictive analytics, DTs enable significant innovation in Prognostics and Health Management methods. This paper explores the application of DTs in health management for complex aircraft systems, focusing on two critical subsystems: Flight Control Electrical Actuators and Main Landing Gear. Leveraging MATLAB Simscape, modular DT frameworks were developed to simulate these systems under nominal, degraded, and fault conditions. The inclusion of fault injection models enables the generation of realistic datasets to support predictive maintenance, alleviating difficulties in data availability. Two case studies are presented to illustrate the potential of DT-based approaches to reduce downtime, optimizing performance, and enhancing system reliability. This paper provides a comparative analysis of existing DT tools, highlighting their capabilities and limitations in aerospace contexts. While platforms such as MATLAB Simulink and ANSYS Twin Builder offer robust modeling capabilities, operational tools like AVIATAR and IBM Maximo excel in fleet management and predictive analytics. This comparison highlights the need for tailored DT solutions that balance real-time capabilities, scalability, and configurability. This study contributes to the growing body of knowledge on DT technology, offering insights into its role in enhancing aviation safety, efficiency, and sustainability. It serves as a guide for applying DT-based health management, paving the way for broader adoption in next-generation aerospace systems.