## 1 Introduction
Numerous studies have confirmed the substantial application value of knowledge graph technology in various industries like electric power equipment [19, 20, 21, 22, 23, 24, 25, 26], network communication [27, 28, 29, 30, 31], and aerospace [32, 33, 34, 35, 36, 37, 38, 39, 40, 41]. In the field of space exploration, several recent studies have emphasized the increasing role of knowledge graphs. [PERSON] [39] et al. pioneered the construction of a knowledge graph for spacecraft launch, addressing issues of sparse and incomplete knowledge and paving the way for semantic AI and complex data analytics. Concurrently, [PERSON] [40] and colleagues introduced an innovative approach to spacecraft fault diagnosis through the integration of information using fusion technologies to construct a comprehensive lifecycle knowledge graph. This approach enhances fault response capabilities and ensures spacecraft integrity. Meanwhile, [PERSON] [41] and his team proposed a knowledge graph specific to embedded aerospace software defects, improving the efficiency of third-party software testing and evaluation and enhancing the quality and credibility of aerospace software products. The contributions of these studies underscore the increasing use of knowledge graphs as an effective tool for managing complex issues in the spacecraft domain. Despite these advancements, the application of knowledge graph technology in spacecraft anomaly detection is still in the preliminary exploration stage. To address this gap, this paper proposes a knowledge graph-based on-orbit spacecraft anomaly detection method. The main contributions of this paper are as follows:1. A knowledge graph-based architecture and framework for spacecraft anomaly detection is proposed, consisting of a data layer, a graph construction layer, an algorithm integration layer, and an application layer;
2. A method for constructing a spacecraft equipment knowledge graph is introduced, integrating expert knowledge and historical data to enhance the interpretability and reliability of anomaly detection;
3. Spacecraft anomaly event knowledge graphs are created based on various anomaly features, facilitating anomaly device location and cause judgment by matching telemetry data with feature data in the knowledge graph;
4. Experimental results demonstrate the effectiveness of the proposed method and its potential applications in spacecraft anomaly detection. The rest of this paper is arranged as follows. Section 2 develops the architecture and framework of the spacecraft anomaly detection algorithm based on a knowledge graph. Section 3 presents the workflow of the spacecraft anomaly detection method based on a knowledge graph. Section 4 discusses typical cases and provides a result analysis. Finally, Section 5 concludes the paper. This research builds on existing efforts by developing a tailored knowledge graph framework for spacecraft anomaly detection. The focus on integrating expert knowledge and historical data into the graphs aims to enhance the interpretability and context awareness of the resulting anomaly detections. By matching real-time telemetry data against learned patterns and relationships in the knowledge graph, the goal is to enable more rapid diagnosis of spacecraft anomalies compared to purely data-driven techniques. The experimental validation on real spacecraft data will further validate the benefits of this knowledge graph approach in supporting spacecraft operations.