A knowledge service framework for fault diagnosis of low-earth orbit satellite constellation

The rapid expansion of low-earth orbit satellite constellations poses a significant challenge for the operation and maintenance of thousands of satellites. In-orbit fault diagnosis helps minimize the cost of repairs, and ensure the overall reliability of the satellite constellation by identifying faults in their early stages. Compared to traditional fault detection methods, fault diagnosis requires knowledge to enhance very limited data and to infer cascading failures. In this paper, we propose a novel knowledge service framework that combines data-driven and knowledge-driven models to improve the efficiency and effectiveness of fault diagnosis. We implement a fault diagnosis service based on a constructed knowledge graph, which affords an assistant decision support function. The in-orbit deployment and simulation experiment verify the feasibility of the proposed framework, which shows great potential for fault diagnosis of low-earth orbit satellite constellation.