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

2026-09-09

AI Summary

本文提出了一种无监督的航天器遥测异常检测框架，通过增量式月度重训练、统计模型选择和自适应极值理论阈值控制，解决了实际中缺乏标注数据的问题。

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.

Problem

• unsupervised anomaly detection
• spacecraft telemetry
• adaptive EVT thresholding

Innovation

• unsupervised anomaly detection
• adaptive EVT thresholding
• incremental retraining