基于LightGBM的卫星飞轮温度预测

为保障卫星的正常在轨运行，地面系统需要对卫星运行状态进行监控预警，其中对卫星各系统的温度监控尤为重要.温度不仅直接反映卫星系统的健康状态，更会对系统器件的性能和寿命造成影响.飞轮作为卫星姿态控制系统的重要组件，其温度变化是识别姿态控制系统状态的重要信息.卫星飞轮温度的预测与预警对卫星在轨稳定运行具有重要意义.本文基于某在轨卫星遥测数据，结合空间环境数据，应用LightGBM机器学习框架研究建立梯度提升决策树模型，对卫星飞轮温度进行预测.经与实际遥测温度值进行对比验证，预测精度可以满足对卫星飞轮温度的监视需求.研究结果可应用于地面系统，对卫星姿态控制系统可能发生的温度异常进行预警，使地面运控人员能够提前规避风险，保障卫星的安全在轨运行. 

Temperature Prediction of Satellite Flywheel Based on LightGBM

In order to ensure the stable operation of satellites, it is important for the ground system to monitor and predict the satellite state, especially the monitoring of flywheel temperature. As an important component of attitude control system of a satellite, the temperature of flywheel is important to identify the state of the system. The prediction of flywheel temperature is of great significance to the stable operation of satellites in orbit. In this paper, based on the LightGBM machine learning framework, a gradient boosting decision tree model is established by using spatial environmental data and in-orbit telemetry data of a satellite, to predict the temperature change of satellite flywheel. By comparing with the actual flywheel temperature, the prediction accuracy can meet the monitoring requirement of satellite flywheel temperature. This model can be applied to warn the ground system the possible temperature anomalies of attitude control system, so that controllers can avoid risks ahead of time and ensure the safe operation of satellites. The research results have certain universality for other satellite flywheel systems.