On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities
2026-04-09 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors focus on making spacecraft fault detection smarter and easier to understand. They introduce a new method that helps explain how a neural network finds problems by creating simple, meaningful summaries called peepholes. These peepholes help spot and locate issues in spacecraft parts like reaction wheels without needing much extra computing power. This approach makes it easier to trust and use these AI systems on spacecraft.
Fault DetectionExplainable AINeural NetworksAutoencoderAnomaly DetectionAttitude and Orbit ControlReaction WheelsTelemetryInterpretability
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
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.