Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.
@article{arxiv.2406.17826,
title = {European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry},
author = {Krzysztof Kotowski and Christoph Haskamp and Jacek Andrzejewski and Bogdan Ruszczak and Jakub Nalepa and Daniel Lakey and Peter Collins and Aybike Kolmas and Mauro Bartesaghi and Jose Martinez-Heras and Gabriele De Canio},
journal= {arXiv preprint arXiv:2406.17826},
year = {2025}
}