The health management of artificial satellites is becoming increasingly important for enhancing operational efficiency and enabling early change detection. In this study, we systematically define and classify the complex and unique characteristics of satellite telemetry data. Furthermore, we introduce Quantile Gaussian Process Regression (Q-GPR) as an evaluation method capable of addressing these challenges. Utilizing real satellite data, we demonstrate that Q-GPR is effective in anomaly detection even for data with coarse quantization levels and datasets that involve multiple operational modes, which have been challenging for conventional methods to handle.
