Abstract
In this article we explore the possibility of finding anomalies automatically on real satellite telemetry. We compare two different machine learning techniques as an alternative to the classic limit control. We try to avoid, as much as possible, the intervention of an expert, detecting anomalies that cannot be found with classical methods or that are unknown in advance. Gaussian Mixture and Simple Rolling Means are used over a low orbit satellite power subsystem telemetry. Some telemetry values are artificially modified to generate a shutdown in a solar panel to try to achieve early detection by context or by comparison. Finally, results and conclusions are presented.
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Soligo, P., Merkel, G., Jorge, I. (2022). Ground Segment Anomaly Detection Using Gaussian Mixture Model and Rolling Means in a Power Satellite Subsystem. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_17
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DOI: https://doi.org/10.1007/978-3-031-05903-2_17
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