基于灰色系统理论的测控设备状态预测算法研究

Research on state prediction algorithm of TT&C equipment based on grey system theory

随着在轨航天器数量的增加,为支持航天器的可靠运行,地面测控设备需长期处于加电状态,这对设备状态检测与维护管理带来较大难度。提出两种基于灰色系统理论的GM和Verhulst测控设备状态预测模型,采用数据平滑和背景值改进等措施,对地面测控设备状态预测模型进行优化,有效提高了预测精度,并通过实例分析,梳理了两种预测模型的适用范围,为开展测控设备视情维修提供决策支持。

In order to support the reliable operation of spacecraft,the ground tracking,telemetry and command(TT&C)equipment need to be in power on state for a long time with the increase of the number of spacecraft in orbit,which brings great difficulty to the equipment state detection and maintenance management.Two state prediction models of GM and Verhulst TT&C equipment based on grey system theory are proposed in this paper.By data smoothing and background value improvement,the state prediction model of ground TT&C equipment is optimized,and the prediction accuracy is effectively improved.Through actual case analysis,the application scope of the two prediction models is sorted out to provide decision support for condition based maintenance of TT&C equipment.