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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08424 |
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| _version_ | 1866908949719023616 |
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| author | Capelli, Lorenzo Rosa, Leandro de Souza De Tommasi, Maurizio Manovi, Livia Enttsel, Andriy Mangia, Mauro Rovatti, Riccardo Pinci, Ilaria Ciancarelli, Carlo Mariotti, Eleonora Furano, Gianluca |
| author_facet | Capelli, Lorenzo Rosa, Leandro de Souza De Tommasi, Maurizio Manovi, Livia Enttsel, Andriy Mangia, Mauro Rovatti, Riccardo Pinci, Ilaria Ciancarelli, Carlo Mariotti, Eleonora Furano, Gianluca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08424 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities Capelli, Lorenzo Rosa, Leandro de Souza De Tommasi, Maurizio Manovi, Livia Enttsel, Andriy Mangia, Mauro Rovatti, Riccardo Pinci, Ilaria Ciancarelli, Carlo Mariotti, Eleonora Furano, Gianluca Artificial Intelligence Machine Learning 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. |
| title | On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.08424 |