Integrated AI-Driven Aircraft Maintenance System with Real-Time Crack Detection, Battery Life Estimation, and Jet Engine Predictive Maintenance

The maintenance of aircraft performs indispensable functions to support safety standards together with operational effectiveness and industrial monetary viability of aviation. This paper introduces a holistic AI-maintained system which executes deep learning and machine learning algorithms for live crack recognition and battery life prediction and jet engine prognostics. This system applies YOLO (You Only Look Once) for reliable aircraft structural damage inspection while machine learning techniques predict battery life through operational and environmental factors and the custom neural network performs jet engine cycle forecasts. The aircraft monitoring system gathers information from various sensors throughout different components of the aircraft to achieve a detailed view of vital hardware details. The platform delivers real-time maintenance monitoring insights to teams through its cloud-based analytics systems which leads to higher decision capability. Ethical data collection from the past enables ongoing maintenance process optimization together with continuous learning improvements. The system brings together these components to form an integrated platform which delivers predictive maintenance whereas it minimizes operational delays as well as operational expenses and strengthens safety protocols. The implemented system provides better predictive accuracy and real-time monitoring abilities than conventional methods based on experimental results.