Novel Anomaly Detection Method for Satellite Power System

被引：0

作者：

Zhang H.-F. ^{[1
]}

Jiang J. ^{[1
]}

Zhang X.-Y. ^{[2
]}

Pi D.-C. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] Beijing Institute of Spacecraft System Engineering, Beijing

来源：

Yuhang Xuebao/Journal of Astronautics | 2019年 / 40卷 / 12期

关键词：

Anomaly detection; Satellite power; Stacked auto encoders; Unsupervised learning;

D O I：

10.3873/j.issn.1000-1328.2019.12.011

中图分类号：

学科分类号：

摘要：

In this paper, a novel representative feature auto-encoder (RFAE) model is proposed and applied to the unsupervised anomaly detection for the high-dimensional periodic time series telemetry data of a satellite power system. RFAE uses the improved stacked auto-encoder loss function and training algorithm, so that the model can learn the representative features of the same phase samples. Then, the samples are reconstructed according to the representative features and the reconstructed error is used to determine whether the samples are abnormal. In the experimental part, firstly, the synthetic data proves that the RFAE algorithm can effectively detect the anomalies of the high-dimensional periodic time series data. Then, the real telemetry data of a satellite power system from January to December 2014 is used to conduct experiment. The accuracy rate of the RFAE anomaly detection reaches 99%, and the detection effect is obviously better than those of other current anomaly detection algorithms. © 2019, Editorial Dept. of JA. All right reserved.

引用

页码：1468 / 1477

页数：9

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