## 1 Introduction  
In recent years, with the continuous growth of the number of on-orbit spacecraft and the increasing complexity of onboard equipment, the frequency of anomalies occurring in spacecraft operations has gradually increased. Due to the high value and difficulty of repairing spacecraft, any anomaly may lead to serious consequences. Therefore, it is significantly important in on-orbit spacecraft health management research to monitor spacecraft status in real time, detect anomalies, and deal with them promptly to prevent failures from occurring. In the field of spacecraft anomaly detection, telemetry data anomaly analysis has received widespread attention, as it is closely related to equipment operating status. Data-driven methods, which have a higher degree of intelligence compared to methods based on thresholds, expert systems, and expert experience, have become a current research hotspot. In recent years, a large number of studies have focused on this field, resulting in various methods, which are reported below. 1. Anomaly detection methods for telemetry data correlation: Various techniques have been proposed to address this issue, including the ARMA prediction model [1], LSSVM [2], RVM [3], KPCA [3], and dynamic Bayesian network [3];
2. Anomaly detection methods for telemetry data clustering: These approaches incorporate methods such as LSSVM [2], hierarchical clustering combined with KNN classification [4], extraction of significant time patterns [5], DTW matching-based techniques [6], and LOF based on statistical eigenvalues [7];
3. Multivariate anomaly detection methods for telemetry data: These techniques can be broadly divided into four categories. First, methods based on subspace computation, including Orca [8], GritBot [9], IMS [10,11], K-means [12], expectation maximization [12], OCSVM [13], and PLSDA [14]. Second, generative estimation-based methods, which consist of the dynamic grouped mixture model [15]. Third, basic pattern reconstruction error-based methods, which encompass sparse representation and latent semantic analysis techniques [16]. Finally, graph construction-based methods, involving box modeling algorithms [17]. Each of these approaches provides unique benefits and applications for tackling the challenges associated with telemetry data anomaly detection. In general, data-driven methods analyze large amounts of historical data to build anomaly detection models for spacecraft systems, allowing for the monitoring of spacecraft status parameters and the detection of some anomalies and failures. However, due to the complexity of spacecraft systems, the interpretability of anomaly detection and fault diagnosis results is limited, and further analysis of anomaly causes still relies on expert knowledge and experience. Additionally, in the spacecraft on-orbit operation management process, operators need to repeatedly memorize and consult maintenance procedures, emergency plans, and troubleshooting manuals, which are inefficient and prone to errors or omissions. A large amount of prior knowledge about anomalies has not been fully utilized, resulting in data resource waste. Therefore, there is an urgent need for an explainable artificial intelligence technology and intelligent knowledge application technology to improve the efficiency of spacecraft anomaly detection and knowledge management. In recent years, knowledge graphs have become an emerging technique for anomaly detection across various domains. For example, [PERSON] et al. [18] surveyed various graph-based anomaly detection methods, highlighting the benefits of modeling anomalies using connectivity patterns. Since Google launched the knowledge graph-based search engine in 2012, the interest in constructing and applying domain-specific knowledge graphs across industries has grown significantly.