Traditional fault management can be an onerous task and robust automated solutions are increasingly necessary to accommodate the complexities of modern space systems and mission operations. The present work proposes a hybrid framework for performing automated spacecraft fault detection by leveraging the benefits of both model-based and data-driven approaches. The framework uses a system model to generate residual data that are subsequently fed into a data-driven residual analysis stage. The framework was verified by using data from a hardware-in-the-loop test campaign in which faults were injected into a spacecraft attitude control system, and successfully identified. The fault detection approach implemented using this framework outperformed results obtained from expert-tuned fault detection parameters. Overall, the proposed framework is a promising alternative for sustainable fault detection and mission operations suitable for complex space systems.
How to Cite
fault management, health monitoring, machine learning, model-based systems engineering
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