## 4 Example
### Telemetry Data Anomaly Detection Experiment
#### 4.3.3 Anomaly Analysis Based on a Knowledge Graph  
During the operation of a spacecraft, the anomaly detection algorithm can identify abnormal telemetry data and output the abnormal parameters and devices. Upon detecting an anomaly, further data analysis can reveal the characteristics of the abnormal phenomenon. The abnormal parameters and phenomena can be organized into keywords and input into a Naive Bayes classifier to calculate the probability distribution of different anomaly events. In this way, the cause of the anomaly event can be quickly located based on the spacecraft anomaly event graph, and corresponding measures can be taken to maintain the normal operation of the spacecraft. Figure 20 shows an experimental case of anomaly analysis for a spacecraft CMG system.  
In this case, further data analysis revealed abnormal fluctuations in telemetry parameters CMG005, CMG013, and CMG017. CMG005 experienced a sudden increase, CMG013 had large fluctuations, and CMG017 showed periodic decreases. By inputting these abnormal parameters and phenomena into the Naive Bayes classifier, the probability distribution of three different anomaly event categories was calculated. According to the classifier's results, event 4 had the highest probability. Therefore, this set of abnormal parameters and phenomena was classified as event 4. Further, using the spacecraft anomaly event graph, the cause of event 4 was quickly identified: a failure in a motor component in the CMG system led to unstable attitude control of the spacecraft. Based on the anomaly handling plan, engineers can troubleshoot the faulty control moment gyroscope component and reset  
Figure 20: Spacecraft CMG system anomaly handling experimental case.  
the drive circuit to restore its normal working status, thereby resolving the spacecraft's anomaly issue.  
In practical applications, incomplete anomaly information may exist. Engineers can determine the priority of anomaly event investigation based on the probability values output by the classification model, improving investigation efficiency. For example, in this case, if some telemetry parameter data were missing, engineers could first investigate event 4 with the highest probability, followed by events 2 and 9. We have conducted further experiments to address the potential issue of the Naive Bayes model's feature independence assumption not being fully valid in practice, which might lead to inaccurate probability estimates. We selected 20 sample events and constructed 20 incomplete input data to evaluate the classifier's performance. The experimental results show that if the event with the highest predicted probability matches the correct label, our accuracy rate can reach 65%. If the correct label is within the top three events with the highest predicted probabilities, our accuracy rate can even reach 100%. This indicates that even if the feature independence assumption might cause inaccurate event probability prediction, the classifier still often makes correct classification decisions, and the Naive Bayes classifier can still achieve satisfactory results.  
Thus, the anomaly analysis method based on the knowledge graph provides effective support for anomaly handling and maintenance during spacecraft operation. While the anomaly analysis method based on the knowledge graph greatly aids anomaly handling and maintenance in spacecraft operation, it is important to acknowledge its limitations. The knowledge graph is confined to the information it contains and may not account for unknown or novel situations. Additionally, its effectiveness is tied to the quality and completeness of data input. Therefore, continuous maintenance and updates by experts are necessary to keep the system current and accurate. The method is not a standalone solution, but a tool that with expert knowledge and ongoing upkeep, can strongly support anomaly analysis and maintenance across various fields.