Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding
cs.LG
Submitted: 2026-09-09
Updated: 2026-09-09
Comments: 5 pages, 4 figures. Accepted as a poster at SPAICE 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods.
Terminology
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.
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