AI-Driven Aircraft Maintenance System for Real-Time Crack Detection and Predictive Maintenance

This paper proposes the development of an advanced AI-driven aircraft maintenance system designed to address critical inefficiencies in traditional maintenance practices. The system integrates multiple deep learning and machine learning models to enhance predictive capabilities. YOLO (You Only Look Once) is employed for real-time crack detection in aircraft structures, offering rapid and accurate identification of structural damage. Machine learning algorithms are utilized to estimate battery life, incorporating real-world data such as charge/discharge cycles and environmental conditions. Additionally, predictive models analyze sensor data from jet engines to forecast their remaining useful life (RUL), allowing for timely interventions. By combining these functionalities into a centralized platform, the proposed system enables proactive maintenance, reduces downtime, enhances safety, and lowers operational costs. This integrated approach transforms conventional reactive and schedule-based maintenance into a dynamic, data-driven framework. The real-time insights and personalized predictions provided by the system empower maintenance teams to address potential failures before they occur, ensuring optimized maintenance schedules and improved operational reliability. By leveraging cutting-edge AI technologies, this research has the potential to revolutionize aircraft maintenance practices, delivering significant advancements in safety, efficiency, and cost-effectiveness within the aviation industry.