基于高斯混合模型的卫星电源系统异常检测方法  

Anomaly Detection for Satellite Power System Based on Gaussian Mixture Model

作　　者：魏居辉 王炯琦[1] 穆京京 何章鸣[1,3] 周萱影 WEI Juhui;WANG Jiongqi;MU Jingjing;HE Zhangming;ZHOU Xuanying(National University of Defense Technology,Changsha 410073,Hunan,China;China Aerospace Science and Technology Corporation,Beijing 100090,China;Beijing Institute of Spacecraft System Engineering,Beijing 100090,China)

机构地区：[1]国防科技大学,长沙410073 [2]中国航天科技集团有限公司,北京100090 [3]北京空间飞行器总体设计部,北京100090

出　　处：《空间控制技术与应用》2022年第4期104-114,共11页Aerospace Control and Application

基　　金：国家自然科学基金资助项目(61903366,61903086,62001115);湖南省自然科学基金资助项目(2019JJ50745,2020JJ4280,2021JJ40133);北京控制工程研究所基金资助项目(HTKJ2019KL502007);民用航天预研基金资助项目(B0103)。

摘　　要：作为具有多种工作模式的复杂系统,卫星电源系统在不同工作模式下的观测数据具备不同的统计特性.因为卫星电源系统的实际观测数据缺少状态标识作为先验信息,所以传统异常检测方法无法区分系统的不同工作模式,具有较大局限性.针对无状态标识的卫星电源系统异常检测问题,提出了一种基于高斯混合模型(GMM)的异常检测方法.高斯混合模型被用于状态标识缺失数据的特征挖掘,从而实现对不同工作模式的聚类与识别;可区分性、稳定性、以及拟合优良性三个指标被用于GMM的评价,使得聚类簇数的选取是合理的;在异常检测阶段,训练好的高斯混合模型被用于构建了模式识别准则,距离信息和F分布被用于构建了检测阈值,并通过增加待检测数据集窗口长度来提升检测效果;以卫星电源系统的太阳能帆板机构为对象,开展了数值仿真和实验验证.异常检测结果表明,该方法能有效实现多种工作模式下的异常检测,具有较高的准确率和召回率.Satellite power system usually has a variety of working modes,and the observation data in different working modes have different statistical characteristics.Due to the fact that the actual observation data of satellite power system cannot provide the state identification priori information,the traditional anomaly detection methods cannot distinguish the different working modes of the satellite power system.Therefore,the traditional methods have great limitations.In order to solve the problem of anomaly detection without state identification,a data-driven anomaly detection method is proposed for satellite power system based on Gaussian mixture model(GMM).As a data clustering method,GMM can mine the intrinsic characteristics of data in the lack of working state identification,and realize the clustering and recognition of multiple working modes.Then,indexes are given to evaluate the GMM method from three aspects:distinguishability,stability and information.These criteria can ensure that the cluster number is reasonable.Furthermore,in the anomaly detection stage,the trained GMM is used to construct the pattern recognition criteria.The distance information and F distribution are used to construct the detection threshold.And the detection effect is improved by increasing the window length of the testing data.Finally,numerical simulation and experimental verification are carried out for the solar array drive assembly(SADA) of satellite power system.The results of anomaly detection show that the proposed method can effectively realize anomaly detection in a variety of working modes,and has high precision and recall rate.

关 键 词：卫星电源系统 异常检测 高斯混合模型 EM算法 状态标识缺失 数据驱动 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]