Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/137381
Title: A review of anomaly detection in spacecraft telemetry data
Authors: Fejjari, Asma
Delavault, Alexis
Camilleri, Robert
Valentino, Gianluca
Keywords: Aerospace telemetry
Space vehicles -- Equipment and supplies
Aerospace engineering -- Data processing
Artificial intelligence -- Industrial applications
Fault location (Engineering) -- Data processing
Issue Date: 2025
Publisher: MDPI AG
Citation: Fejjari, A., Delavault, A., Camilleri, R., & Valentino, G. (2025). A Review of Anomaly Detection in Spacecraft Telemetry Data. Applied Sciences, 15(10), 5653.
Abstract: 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.
URI: https://www.um.edu.mt/library/oar/handle/123456789/137381
Appears in Collections:Scholarly works - InsAT

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