Electromechanical actuators have become key components in next-generation aircraft architectures, particularly under More Electric Aircraft and Power-by-Wire paradigms. However, their operational complexity, compounded by mechanical-electrical interactions, introduces failure modes that are both difficult to detect and insufficiently represented in existing datasets. This paper presents a comprehensive digital twin-based framework developed to simulate and analyse EMA behaviour under both nominal and faulty conditions. Implemented using MATLAB Simulink and Simscape, the framework comprises modular voltage and load profiles, structured fault injection mechanisms, and labelled data generation tools. This work investigates the signal responses to various fault types, including mechanical backlash, voltage drop, and electrical resistance anomalies, both in isolation and combination. The simulation output enables systematic feature extraction and evaluation for diagnostics and health indicator development. The framework generated a high-fidelity dataset of 70,000 labelled samples, which demonstrated excellent feature separability for both single and compound faults under Principal Component Analysis. This research addresses the aerospace industry’s pressing need for synthetic, fault-labelled data to train and validate diagnostic algorithms and offers a scalable methodology applicable to diverse actuator configurations. The resulting openable, labelled dataset and modular scripts enable reproducible benchmarking for EMA health monitoring and will be extended to physics-informed prognostics in subsequent work.
Wang et al. (Fri,) studied this question.