A Review of Anomaly Detection in Spacecraft Telemetry Data

• Published in: Applied sciences Vol. 15; no. 10; p. 5653
• Main Authors: Fejjari, Asma, Delavault, Alexis, Camilleri, Robert, Valentino, Gianluca
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: anomalies; anomaly detection; Artificial intelligence; Communications satellites; Deep learning; Ground stations; Machine learning; Methods; Radiation; Rocket launches; Space ships; Space vehicles; spacecraft; Subject specialists; Telemetry; telemetry data; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15105653

Telemetry data play a pivotal role in ensuring the success of spacecraft missions and safeguarding the integrity of spacecraft systems. Therefore, the timely detection and subsequent notification of any abnormal events related to the functionality of spacecraft subsystems are crucial to ensure their safe operation. In recent years, several anomaly detection methods have been developed to monitor spacecraft telemetry data and detect anomalies. This manuscript provides a comprehensive literature review of the existing anomaly detection methods for spacecraft telemetry data. It exposes the challenges faced by such systems, highlights the strengths and limitations of each anomaly detection method, and assesses and compares the performance of these approaches in detecting anomalies. Initial results show that GCN and TCN models have achieved promising precision up to 94%. The paper concludes with a series of recommendations and the potential research directions.