Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection

Featured Application: This method applies knowledge graph technology for spacecraft anomaly detection, improving reliability and safety in space missions. It enables real-time monitoring, timely diagnosis, and maintenance, preventing mission failures. Potential applications include predictive maintenance strategies, resource optimization, and proactive planning. The approach advances space system engineering and enhances the robustness of future missions. Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.