European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

Krzysztof Kotowski; Christoph Haskamp; Jacek Andrzejewski; Bogdan Ruszczak; Jakub Nalepa; Daniel Lakey; Peter Collins; Aybike Kolmas; Mauro Bartesaghi; Jose Martinez-Heras; Gabriele De Canio

arXiv · 2406.17826

European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

Krzysztof Kotowski·Christoph Haskamp·Jacek Andrzejewski·Bogdan Ruszczak·Jakub Nalepa·Daniel Lakey·Peter Collins·Aybike Kolmas·Mauro Bartesaghi·Jose Martinez-Heras·Gabriele De Canio

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Abstract

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.

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Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan Ruszczak, Jakub Nalepa, Daniel Lakey, Peter Collins, Aybike Kolmas, Mauro Bartesaghi, Jose Martinez-Heras, Gabriele De Canio. 2024-06-25. European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry. https://arxiv.org/abs/2406.17826

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