This work proposes an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning.
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.
Spacecraft telemetry anomaly detection must operate under stringent operational constraints: physically heterogeneous channels, rare unlabeled faults, a nominal regime contaminated by sensor glitches and drifting across mission phases, resource-limited online execution, and a requirement to explain every alarm. Simple...
The increasing complexity of modern satellites and the growing amount of telemetry data available pose significant challenges for a safe and economic operation of satellites. To support the satellite engineers, traditional machine learning methods, including deep learning-based approaches, have shown promising result...
Schefels Clemens, Balan Arvind, B. Bilel et al.· CEAS Space Journal· 0 citations
An adaptive robust reconstruction strategy integrated into a hybrid modeling framework combining long short-term memory and self-attention mechanisms is proposed, which outperforms traditional approaches in nearly all detection performance metrics, and achieves both lower false negative and false alarm rates.
Ju-Hui Wei, Jiong-Qi Wang, Peng-Yi Wang et al.· Measurement science and tech...· 0 citations
Normalizing flows are principled anomaly detectors, selecting anomalies using a probabilistic per-event likelihood. Extreme latency and resource constraints have prevented the deployment of flow likelihoods within the hardware triggers at the Large Hadron Collider. We bypass these limitations by distilling the likeliho...
Tara P. A. Tahseen, N. Hall, Nikolaos Konstantinidis et al.· 3 citations
The findings show that a self-recalibrating unsupervised model can successfully detect faults in a scalable manner, with an interpretable model, low computational complexity and can adapt to ageing assets and varying operating baselines without labelled fault information.
D. Singh, Durga Prasad Panday, Manish Kumar· International journal of com...· 0 citations
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