College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2022 Aug 24;22(17):6358. doi: 10.3390/s22176358.
Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.
基于遥测数据的异常检测是卫星健康监测中的一个主要问题，它可以识别异常或意外事件，有助于避免严重事故，确保操作的安全性和可靠性。近年来，稀疏表示技术在异常检测中受到越来越多的关注，尽管它在卫星中的应用仍在探索之中。本文提出了一种新的基于稀疏特征的异常检测方法（SFAD），用于识别遥测中的混合异常。首先，通过 K-均值奇异值分解（K-SVD）算法获得遥测数据字典和相应的稀疏矩阵，然后从包含多元遥测时间序列局部动态和共同发生关系的稀疏矩阵中定义稀疏特征。最后，将低维稀疏特征向量输入到单类支持向量机（OCSVM）中，以检测遥测中的异常。基于卫星天线遥测数据的案例分析表明，与其他现有的多元异常检测方法相比，所提出的方法提高了检测精度、F1 分数和 FPR，表明该方法具有良好的有效性。
