Summary of European Space Agency Benchmark For Anomaly Detection in Satellite Telemetry, by Krzysztof Kotowski et al.
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
by Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan Ruszczak, Jakub Nalepa, Daniel Lakey, Peter Collins, Aybike Kolmas, Mauro Bartesaghi, Jose Martinez-Heras, Gabriele De Canio
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark for multivariate time series anomaly detection in satellite telemetry, the European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB), is introduced to improve machine learning-based solutions. This benchmark addresses a significant challenge by providing comprehensible evaluation metrics and annotated real-life telemetry data from three ESA missions. The hierarchical evaluation pipeline highlights the need for novel approaches to meet operators’ needs, while the publicly available dataset ensures full reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection in satellite telemetry is important for spacecraft operations. Currently, there’s no good way to test machine learning models that do this job. To solve this problem, experts from the European Space Agency (ESA) and machine learning specialists worked together. They created a new dataset with real-life data from three ESA missions, which can be used to test anomaly detection algorithms. The results show that we need new approaches to solve this challenge. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning » Time series
