对比机器学习与深度学习在航天姿态传感器异常检测中的表现
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors
- 比较传统机器学习与深度学习在多变量时间序列异常检测中的效果
- 深度学习在识别卡死值方面表现更优，但可解释性较差
- 适合关注航天系统故障诊断的工程师与研究者
在航天器故障检测、隔离与恢复（FDIR）框架下，基于人工智能的新方法正在克服传统阈值检查的局限性。本研究旨在分析两种不同方法在航天器姿态传感器多变量时间序列中卡死值检测问题上的表现。结果揭示了两种方法在检测性能上的差异，并讨论了它们在可解释性及跨场景泛化能力方面的优劣。
原文摘要 · Abstract (English)
In the framework of Failure Detection, Isolation and Recovery (FDIR) on spacecraft, new AI-based approaches are emerging in the state of the art to overcome the limitations commonly imposed by traditional threshold checking. The present research aims at characterizing two different approaches to the problem of stuck values detection in multivariate time series coming from spacecraft attitude sensors. The analysis reveals the performance differences in the two approaches, while commenting on their interpretability and generalization to different scenarios.
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