Purpose

The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.

Design/methodology/approach

This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.

Findings

Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.

Originality/value

This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

中文翻译：

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一种深度自编码器卫星异常预警框架

目的

本文旨在保障卫星在轨稳定运行，协助地面人员持续监测卫星遥测数据，提前发现异常，提高卫星运行可靠性，防止灾难性损失。

设计/方法/方法

本文提出了一种用于卫星遥测数据的深度自动编码器（DAE）卫星异常预警框架。首先，本研究进行灰色相关分析，提取重要特征属性构建特征向量，并构建具有双向长短期记忆生成对抗网络鉴别器（VAE/BLGAN）的变分自动编码器。然后，使用马氏距离来衡量输入和输出的重建分数。根据卫星运行的周期性特点，提出了一种基于周期性时间窗的动态门限方法。卫星健康监测和预警是使用重建分数和动态阈值实现的。

发现

实验结果表明，DAE 方法可以探测卫星遥测数据出现异常，在异常发生之前触发警告，从而有足够的时间进行故障排除。本文进一步验证了所提出的VAE/BLGAN模型比其他两种自动编码器模型具有更强的数据学习能力，并且对卫星监测数据敏感。

原创性/价值

本文提供了一个DAE框架应用于卫星健康监测和异常预警领域。据作者所知，这是第一篇将DAE方法与卫星异常检测相结合的论文，可以促进人工智能在航天器健康监测中的应用。