Enhanced Fatigue Monitoring in Aircraft Wing Structures using Smart Sensor Integration

This paper proposes a smart fatigue monitoring system for aircraft wing structures, utilizing piezoelectric sensors, to detect stress, and actuators to mitigate risks in real-time. The integration of advanced machine learning algorithms, enables the analysis of sensor data against established thresholds, facilitating accurate predictions of potential fatigue conditions. A SolidWorks simulation focusing on a NACA 4412 airfoil wing is conducted to evaluate the system’s effectiveness under gravity, lift, and drag loads. Additionally, the paper discusses essential design considerations and provides a preliminary cost analysis. A Python-Based simulation is considered to further assess the system’s performance, achieving an accuracy of 97 % for the predictions models, with precision and recall metrics indicating robust classification capabilities. Hence, the suggested scheme promises high potential to enhance the monitoring and management of structural fatigue in aviation, ultimately leading to improved safety, reduced maintenance costs, and extended asset lifespan.