تقرير
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
| العنوان: | European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry |
|---|---|
| المؤلفون: | Kotowski, Krzysztof, Haskamp, Christoph, Andrzejewski, Jacek, Ruszczak, Bogdan, Nalepa, Jakub, Lakey, Daniel, Collins, Peter, Kolmas, Aybike, Bartesaghi, Mauro, Martinez-Heras, Jose, De Canio, Gabriele |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: 87 pages, 24 figures, 19 tables |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2406.17826Test |
| رقم الانضمام: | edsarx.2406.17826 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |