European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

• URL: http://arxiv.org/abs/2406.17826v1
• Date: Tue, 25 Jun 2024 13:23:37 GMT
• Title: European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
• Authors: Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan Ruszczak, Jakub Nalepa, Daniel Lakey, Peter Collins, Aybike Kolmas, Mauro Bartesaghi, Jose Martinez-Heras, Gabriele De Canio,
• Abstract summary: The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to establish a new standard in the domain. The newly introduced ESA Anomalies dataset contains annotated real-life telemetry from three different ESA missions. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs.
• Score: 2.0880207832785436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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