为自主卫星设计可解释的故障检测系统，提升太空任务安全性。
On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities
- 用神经网络中间层生成语义化编码，实现故障可解释性。
- 在反应轮遥测数据中准确定位异常，误报率低。
- 计算开销小，适合部署在资源受限的卫星上。
随着航天器自主性提升，亟需可靠且可解释的故障检测系统。本文针对姿态与轨道控制子系统中的故障检测、隔离与恢复问题，提出一种基于可解释人工智能的框架。通过从神经网络中间激活中提取低维语义标注编码（称为peepholes），该方法增强了卷积自编码器的可解释性。该框架能生成可理解的异常指标，有效识别并定位反应轮遥测数据中的异常。此外，peepholes分析揭示了检测偏差，支持故障精确定位。该方案在仅增加微小计算开销的前提下，实现了异常的语义表征，具备星载部署可行性。
原文摘要 · Abstract (English)
The increasing autonomy of spacecraft demands fault-detection systems that are both reliable and explainable. This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Control Subsystem by introducing a framework that enhances interpretability in neural anomaly detectors. We propose a method to derive low-dimensional, semantically annotated encodings from intermediate neural activations, called peepholes. Applied to a convolutional autoencoder, the framework produces interpretable indicators that enable the identification and localization of anomalies in reaction-wheel telemetry. Peepholes analysis further reveals bias detection and supports fault localization. The proposed framework enables the semantic characterization of detected anomalies while requiring only a marginal increase in computational resources, thus supporting its feasibility for on-board deployment.
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