基于主成分分析的航天器姿态控制系统异常检测

Feng Bingqing, Hu Shaolin, Li Chuan, Miao Yangfan
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引用次数: 2

摘要

针对航天器运行过程中姿态控制系统的高故障率问题,分析了姿态控制系统产生的不同类型数据的变化趋势、特征和规律。本文利用正常运行的数据,构建了主成分分析(PCA)模型来表达变量之间的相关性。在此基础上,提出了基于pca的姿态控制系统异常检测算法,并通过监测多变量统计量来判断姿态控制系统的状态,实现了对在轨航天器的性能监测。实验结果表明,基于PCA模型的多变量统计量检测航天器姿态控制系统异常的方法是可行和有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly detection of spacecraft attitude control system based on principal component analysis
Based on the high fault rate of the attitude control system in the process of spacecraft running, this paper analyzes the variation tendency, characteristics and regularities of different types of data generated by the attitude control system. In this paper, we construct the principal component analysis (PCA) model to express the correlations among the variables using the data collected with normal operation. Then we propose the algorithm of PCA-based anomaly detection of the attitude control system and judge the state of the system by monitoring multi-variable statistics which can implement the performance monitoring of the orbiting spacecraft. The experimental results indicate the feasibility and validity of the method to detect the abnormality of the spacecraft attitude control system based on multi-variable statistics of the PCA model.
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