Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry

• Published in: CEAS space journal 2024-03, Vol.16 (2), p.225-237
• Main Authors: Herrmann, Lars, Bieber, Marie, Verhagen, Wim J. C., Cosson, Fabrice, Santos, Bruno F.
• Format: Article
• Language: English
• Subjects: Aerospace Technology and Astronautics; Algorithms; Anomalies; Best practice; Complex systems; Datasets; Engineering; Error analysis; Original Paper; Satellites; Spacecraft; Telemetry
• ISSN: 1868-2502; 1868-2510
• DOI: 10.1007/s12567-023-00529-5

As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios.