European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

TL;DR

This paper introduces the ESA-ADB benchmark dataset for satellite telemetry anomaly detection, providing a standardized, real-world dataset to advance machine learning methods in spacecraft operations.

Contribution

It presents a new benchmark dataset with annotated telemetry from ESA missions and a hierarchical evaluation pipeline for anomaly detection algorithms.

Findings

Existing algorithms need improvement for satellite telemetry anomalies

ESA-ADB is publicly available for reproducibility and benchmarking

Results highlight the necessity for novel approaches in the domain

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.