A Hybrid Model-Based and Data-Driven Framework for Automated Spacecraft Fault Detection

Abstract

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.