Transformer-based anomaly detection for satellite telemetry data

doi:10.1016/j.actaastro.2025.09.035

2025-09-23

PRE

DOI：10.1016/j.actaastro.2025.09.035

原文链接

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摘要

En 中文

• 呈现基于Transformer的时间序列异常检测的最先进模型。 • 比较各种基于Transformer的方法在异常检测中的有效性。 • 提供对基于Transformer模型的全面且现实的评估。

Keyword:

Time series

Anomaly detection

Satellite telemetry data

Transformers

ESA OPS-SAT

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Acta Astronautica

IF:

3.4

论文数:

1.2W

被引数:

2.1W

机构

University of Malta

学者数:

2.4K

论文数: 2.1K

被引数: 3.1K

引用论文

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IF0

2022-08-21

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Z. P. Xu; Z. J. Cheng; J. H. He; B. Guo

Machine Learning Detects Anomalies in OPS-SAT Telemetry 机器学习检测OPS-SAT遥测数据中的异常

Lecture Notes in Computer Science

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2023-06-26

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TranAD

Proceedings of the VLDB Endowment

IF0

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Satellite Telemetry Data Anomaly Detection Using Bi-LSTM Prediction Based Model

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PREAI

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Deep learning 深度学习

Nature

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