Research on risk prediction of civil aircraft based on intelligent algorithms

Chen Hu

doi:10.1117/12.3051675

18 November 2024 Research on risk prediction of civil aircraft based on intelligent algorithms

Chen Hu

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030A (2024) https://doi.org/10.1117/12.3051675
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In the traditional civil aviation transportation industry, airlines have limited means of obtaining aircraft operational data, lack algorithm models that match the health status, and lack application of analysis of fault modes and impacts of key aircraft systems, as well as solutions to fault problems, which prevents a comprehensive understanding of the aircraft's health status and prediction of health trends. To reduce the operational risks of civil aircraft, this study analyzes the current research status of civil aircraft operational risks and proposes a single-aircraft risk assessment method that combines the Fault Mode and Effect Analysis (FMEA) with Fault Tree Analysis (FTA) for individual aircraft. For the fleet, the study first estimates the exposure frequency of risks using historical operational data of civil aircraft through maintenance planning; then, it establishes risk calculation models and an uncensored fleet risk analysis method based on the Weibull bathtub curve's random failure period and wear-out failure period, and uses actual examples to obtain the fleet's risk change trend and the faced risk values. By analyzing and summarizing the above methods, three improvement directions for reducing operational risks are proposed, effectively reducing the operational risks of civil aircraft.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chen Hu "Research on risk prediction of civil aircraft based on intelligent algorithms", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030A (18 November 2024); https://doi.org/10.1117/12.3051675

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