European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
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arXiv
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| Format: | Preprint |
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2024
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| author | Kotowski, Krzysztof Haskamp, Christoph Andrzejewski, Jacek Ruszczak, Bogdan Nalepa, Jakub Lakey, Daniel Collins, Peter Kolmas, Aybike Bartesaghi, Mauro Martinez-Heras, Jose De Canio, Gabriele |
| author_facet | Kotowski, Krzysztof Haskamp, Christoph Andrzejewski, Jacek Ruszczak, Bogdan Nalepa, Jakub Lakey, Daniel Collins, Peter Kolmas, Aybike Bartesaghi, Mauro Martinez-Heras, Jose De Canio, Gabriele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17826 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry Kotowski, Krzysztof Haskamp, Christoph Andrzejewski, Jacek Ruszczak, Bogdan Nalepa, Jakub Lakey, Daniel Collins, Peter Kolmas, Aybike Bartesaghi, Mauro Martinez-Heras, Jose De Canio, Gabriele Machine Learning Artificial Intelligence 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. |
| title | European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.17826 |