On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities
This addresses the problem of fault detection for autonomous spacecraft, but it is incremental as it builds on existing neural methods with added explainability.
The paper tackles the need for reliable and explainable fault-detection in autonomous satellites by introducing a framework that enhances interpretability in neural anomaly detectors, resulting in interpretable indicators for identifying and localizing anomalies in reaction-wheel telemetry with only a marginal increase in computational resources.
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.