On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

• URL: http://arxiv.org/abs/2604.08424v1
• Date: Thu, 09 Apr 2026 16:25:53 GMT
• Title: On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities
• Abstract summary: This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Control Subsystem.<n>We propose a method to derive low-dimensional, semantically annotated encodings from intermediate neural activations, called peepholes.
• Score: 1.5949218056028653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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