Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding

Abstract:Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves $F_{0.5}=0.700$ on Mission~1 and $F_{0.5}=0.698$ on Mission~2 under strict chronological evaluation.

5 pages, 4 figures. Accepted as a poster at SPAICE 2026

Machine Learning (cs.LG)

arXiv:2609.10017 [cs.LG]

(or arXiv:2609.10017v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.10017 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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