Artificial Intelligence for Autonomous Fault Detection in Satellites

Vishnu Kumar Kaliappan

Abstract


This paper investigates the application of artificial intelligence (AI) in enhancing fault detection capabilities for satellites. We propose a framework that leverages machine learning algorithms to monitor satellite health and performance in real time, allowing for early identification of anomalies and potential failures. The study highlights various AI techniques, including predictive maintenance and anomaly detection, and discusses their integration into existing satellite systems. Experimental results demonstrate the effectiveness of AI-driven approaches in improving reliability and operational efficiency. This research contributes to the ongoing advancement of autonomous satellite systems, ensuring mission success and longevity.

Keywords


fault detection, artificial intelligence, satellites, predictive maintenance

References


Ezzat, D., Hassanien, A. E., Darwish, A., Yahia, M., Ahmed, A., & Abdelghafar, S. (2021). Multi-objective hybrid artificial intelligence approach for fault diagnosis of aerospace systems. IEEE Access, 9, 41717-41730.

Feruglio, L. (2017). Artificial intelligence for small satellites mission autonomy. Politecnico di Torino, 165.

Simon, M. (2020, March). Using Fault Detection/Identification Software with Local Awareness Sensors to Improve Satellite Resiliency. In 2020 IEEE Aerospace Conference (pp. 1-6). IEEE.

Song, K., Zhao, C., Liu, J., Zhang, H., & Li, Z. (2021, August). Research ON satellites fault diagnosis method based on artificial intelligence. In Journal of Physics: Conference Series (Vol. 2005, No. 1, p. 012102). IOP Publishing.


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