基于遥测数据动态特征的卫星异常检测方法

基于遥测参数分析异常是保证卫星正常在轨运行的基础，通常采用阈值法判断遥测参数是否超差来判断卫星工作状态，由于其无法检测在阈值范围内变化的卫星遥测数据异常，因而会导致故障漏报.本文利用遥测参数动态变化特性，提出一种基于遥测数据变化规律检测异常的方法.利用周期图谱法求解遥测参数周期，根据遥测数据各周期之间参数值的相似性，按照遥测参数周期对数据进行采样，得到平稳差分序列，对其建立自回归移动平均混合模型，通过精确的预测结果与实测遥测数据比较来发现异常.利用该方法对实际在轨运行的某卫星2012年5月太阳能帆板转动异常故障进行验证，结果表明其能够有效避免故障漏报. 

Fault detection for in-orbit satellites using an adaptive prediction model

Faults are unavoidable for in-orbit satellites. They are able to be detected by carefully analyzing the telemetry data from these satellites. It is obvious that a good performance of anomaly detection algorithm is very important. This paper presents a new approach which takes the advantage of the similarity of preceding telemetry data to detect current anomaly. Because of its adaptive checking criteria, it can find some faults that cannot be found by some traditional methods. Most of important thing of this algorithm is that no prior knowledge about any particular satellite is needed except telemetry data themselves. In order to prove its availability, we use this new approach to process telemetry data from a China on-orbit satellite in 2012 and a fault is detected instantaneously.