面向卫星电源系统的一种新颖异常检测方法

面向卫星电源高维周期性时序遥测数据，提出了一种新颖的代表性特征自编码器（RFAE）模型，并用于无监督的异常检测。RFAE采用改进的堆叠自编码器损失函数和训练算法，从而使模型可以学习到相位相同样本的代表性特征；然后根据代表性特征重构样本，根据重构误差来判断样本是否异常。在试验部分首先通过模拟数据校验了RFAE算法能够有效地检测出高维周期性时序数据的异常，然后又采用某卫星电源系统2014年1~12月真实遥测数据进行试验，RFAE异常检测准确率达到99%，检测效果明显优于目前的其他异常检测算法，具有较高应用价值。

Novel Anomaly Detection Method for Satellite Power System

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.