Advanced approach in satellite failure detection and predicting using machine learning techniques: Alsat-1B case
by Ali Kaddouri; Saiah Bekkar Djelloul Saiah
International Journal of Space Science and Engineering (IJSPACESE), Vol. 7, No. 1, 2024

Abstract: In this paper, we propose a novel approach for detecting and predicting satellite failures using machine learning, with a focus on the Algerian satellite Alsat-1B. Analysing five years of event data, comprising over seven million occurrences and 3,000 event types, we evaluate four sequence-to-sequence prediction models and eight classification models. Our key contribution combines a Markov chain for sequence prediction and a logistic regression model for classification, proving highly effective with 97.7% accuracy, precision of 1.0, recall of 0.84, and an f-score of 0.91. This approach showcases the potential of intelligent systems in satellite control, underscoring the imperative for further exploration and development in this promising field.

Online publication date: Mon, 01-Jul-2024

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