Anomaly Detection in Spacecraft Telemetry: Forecasting vs. Classification
Daniel T. Lakey, Tim Schlippe · 2024
Anomaly detection in spacecraft telemetry is critical for the success and safety of space missions. Traditional methods often rely on forecasting and threshold techniques to identify anomalies [1]–[5]. This paper presents a comprehensive comparison of traditional forecast-based anomaly detection against two innovative classification methods, including a direct classification and an image classification through Gramian Angular Field (GAF) transforms [6], which have only been analysed in other domains but not for spacecraft anomaly detection. All our investigated systems leverage deep learning architectures and use the popular real SMAP/MSL spacecraft data from [2]. Our findings suggest that direct classification provides a marginal but statistically significant improvement in anomaly detection over traditional methods. However, image classification, while less successful, offers promising directions for future research. The study aims to guide the selection of appropriate anomaly detection techniques for spacecraft telemetry and contribute to the advancement of automated monitoring systems in space missions.