Satellite Fault Detection and Diagnosis based on Data Compression and Improved Decision Tree

Human exploration of space is always ongoing, and satellites play a huge role in this process. Therefore, the stable operation of satellites is very important, and how to detect and diagnose satellite faults has become a key issue. This paper presents a method based on data compression and improved decision tree algorithm, which has improved in both the accuracy and efficiency of satellite fault diagnosis and detection. In the experiment, we used the actually collected communication satellite telemetry data as the original data set to verify the performance and efficiency of the method proposed in this article. The results show that our method can save computing resources and significantly improve model training efficiency; at the same time, it can also resist overfitting and improve detection accuracy. This method can provide an effective solution for satellite fault detection and diagnosis.