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Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems

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  • Igor Kabashkin

    (Engineering Faculty, Transport and Telecommunication Institute, Lauvas iela 2, LV-1019 Riga, Latvia)

  • Leonid Shoshin

    (Sky Net Technics, Business Center 03, Ras Al-Khaimah B04-223, United Arab Emirates)

Abstract

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation.

Suggested Citation

  • Igor Kabashkin & Leonid Shoshin, 2024. "Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems," Future Internet, MDPI, vol. 16(8), pages 1-33, August.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:276-:d:1448881
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    References listed on IDEAS

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    1. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
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