AI and Evolutionary Computation for Intelligent Aviation Health Monitoring

Abstract

This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework employs deep learning models for fault detection, achieving about 97% classification accuracy with an F1-score of 0.97, while remaining useful life prediction yields an R² score of 0.89 with a mean absolute error of 9.8 h. Evolutionary algorithms optimize maintenance strategies, reducing downtime and costs by up to 22% compared to traditional methods. The methodology includes robust data processing protocols, feature engineering techniques, and a modular system architecture supporting real-time monitoring and decision-making. Simulation experiments demonstrate the framework’s effectiveness in balancing maintenance objectives while maintaining high reliability. The research provides practical implementation guidelines and addresses key challenges in computational efficiency, data quality, and system integration. The results show significant improvements in maintenance planning efficiency and system reliability compared to traditional approaches. The framework’s modular design enables scalability and adaptation to various aircraft systems, offering broader applications in complex technical system maintenance.