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

Authors:Lorenzo Capelli, Leandro de Souza Rosa, Maurizio De Tommasi, Livia Manovi, Andriy Enttsel, Mauro Mangia, Riccardo Rovatti, Ilaria Pinci, Carlo Ciancarelli, Eleonora Mariotti, Gianluca Furano View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.08424 [cs.AI] (or arXiv:2604.08424v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.08424 arXiv-issued DOI via DataCite (pending registration) Submission history From: Lorenzo Capelli [view email] [v1] Thu, 9 Apr 2026 16:25:53 UTC (6,982 KB)