Abstract

Modern aircraft generate terabytes of sensor data during every flight. Artificial Intelligence enables predictive maintenance by identifying component degradation before failures occur.

Objectives
Detect early equipment faults.
Reduce aircraft downtime.
Improve maintenance scheduling.
Increase fleet operational reliability.
Methodology

Historical aircraft sensor datasets were processed using machine learning models including Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. Feature engineering was applied to engine vibration, fuel flow, oil pressure, and temperature parameters.

Results

The AI-based maintenance model achieved approximately 95% fault prediction accuracy while reducing unscheduled maintenance events by nearly 30%. Engine health monitoring demonstrated the highest prediction performance.

Findings
AI significantly improves maintenance planning.
Sensor fusion enhances prediction accuracy.
Predictive maintenance reduces operating costs.
Digital twins improve fleet management efficiency.
Conclusion

Artificial Intelligence is transforming commercial aviation maintenance through real-time monitoring, predictive analytics, and automated decision support, contributing to improved safety and lower lifecycle costs.