遥测数据反映了航天器有效载荷的状态和航天器的运行情况,基于遥测数据的航天器故障检测具有广泛的工程实用性。针对航天器遥测数据中含有大量冗余信息的实际情况,文中采用主成分分析理论,对高维遥测数据进行降维处理,在保证原始数据信息损失较小的情况下,从初始高维数据集合中提取低维特征组合,并在此基础上设计了航天器故障定位检测算法。仿真结果表明,文中算法在在轨航天器故障诊断中具有实用性和有效性。

Telemetry data reflects the spacecraft payload's operational state and the spacecraft's operating condition, telemetry data-driven fault detection of spacecraft has great practicability. Considering that a large amount of redundant information contained in the spacecraft telemetry data, principal component analysis(PCA) was adopted to deal with high-dimension telemetry data's dimension reduction in this paper and low-dimensional feature combination was extracted from the initial high-dimensional data set combination. Then an algorithm for spacecraft fault detection and loctation was given. Finally, the validity and practicability of the algorithms were showed by simulation.