Modern aircraft maintenance has requirements for smart systems, which are capable of predicting failures, detecting structural defects and estimating the lifespan of components in real time. This paper introduces an integrated AI embedded-architecture for aircraft maintenance using all-in-one predictive analysis, real-time crack detection and battery RUL estimation. The proposed methodology is based on Random Forest and Support Vector Regression models to predict the degradation of components from sensor data, and a YOLO-based deep learning model for detection of cracks in real-time from high-resolution images of inspection of the aircraft. Also, a Long Short-Term Memory (LSTM) network is used to model the battery degradation and accurately predict RUL with the help of time series parameters of battery operation such as voltage, temperature, and charge cycle. Experimental evaluation shows improvement of fitness, detection of fault, cases of maintenance failures and downtime, and operational safety in comparison with traditional maintenance strategies. The proposed framework provides a way of proactive maintenance planning and also a scalable solution for the next generation intelligent health monitoring systems for aircrafts.

The AI-driven aircraft maintenance system developed in this research provides a robust solution for predictive maintenance, crack detection, and battery life estimation in the aviation industry. By leveraging machine learning models, including Random Forest, YOLO (You Only Look Once), and Long Short Term Memory (LSTM), the system is capable of offering real-time predictions, accurate crack detection, and battery life estimations, all of which contribute to the safety, efficiency, and reliability of aircraft operations. The Random Forest models provided highly accurate predictions for the remaining useful life (RUL) of critical components such as batteries. These models enable proactive maintenance scheduling, reducing unplanned downtime and optimizing maintenance costs. The YOLO model demonstrated strong performance in real-time crack detection in aircraft components. It achieved a high precision (92%) and recall (88%) rate, making it a valuable tool for ensuring the structural integrity of aircraft by detecting cracks early, thus improving aircraft safety. The LSTM model effectively predicted battery life by analyzing historical data related to charge cycles and environmental conditions. It was successful in estimating the remaining useful life (RUL) of aircraft batteries with an RMSE of 0.18 cycles, ensuring timely battery replacements and reducing the likelihood of unexpected battery failures. The integration of these models into a unified system proved successful, enabling real-time monitoring, detection, and prediction. The user interface displayed actionable insights in the form of alerts and health status updates, allowing maintenance personnel to make informed decisions and act proactively. This system shows the potential of AI and machine learning in transforming traditional aircraft maintenance practices by enabling more efficient, cost-effective, and safer operations. It integrates cutting-edge technologies to automate the detection of issues and facilitate predictive decision-making, marking a significant step forward in modernizing aircraft maintenance.

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