Abstract

We survey the progress in data mining methods for spacecraft health monitoring. The main emphasis is placed on the analysis of telemetry data enabling the identification of spacecraft states that are atypical during normal operation and the prediction of possible failures in the operation of the spacecraft or its components. The main stages required for the creation of general-purpose spacecraft state monitoring systems are considered; methods for detecting anomalies in telemetry data taking into account the specific features of the spacecraft are presented in detail; and publications on this topic known to the authors are analyzed. Examples of the implementation of such systems in flight control centers of various countries are given. The promising areas of development of methods for analyzing the technical state of complex systems relevant for solving problems in space technology are discussed, and the main factors that hinder the development of machine learning methods for analyzing telemetry data are noted.

中文翻译：

---

基于遥测的航天器健康监测的现代机器学习方法

摘要

我们调查了航天器健康监测数据挖掘方法的进展。主要重点放在分析遥测数据上，以便识别正常运行期间非典型的航天器状态，并预测航天器或其部件运行中可能出现的故障。考虑了创建通用航天器状态监测系统所需的主要阶段；详细介绍了在考虑到航天器的具体特征的情况下检测遥测数据异常的方法；并对作者已知的有关该主题的出版物进行了分析。给出了在各国飞行控制中心实施此类系统的示例。