Securing Aircraft Maintenance Data with Predictive Insights

The aviation industry relies on accurate and secure maintenance data to ensure aircraft safety, operational efficiency, and regulatory compliance. Traditional maintenance management systems often face challenges such as data tampering, inefficient record-keeping, and delayed fault detection. This paper proposes an innovative aircraft maintenance system that integrates blockchain technology with machine learning to enhance security, reliability, and predictive capabilities. Blockchain ensures immutability and transparency of maintenance records, preventing unauthorized modifications and enabling trust among stakeholders. Meanwhile, machine learning models analyze real-time sensor data to predict the Remaining Useful Life (RUL) of aircraft components, enabling proactive maintenance scheduling. Smart contracts further automate maintenance validation and alerting, reducing manual intervention and improving workflow efficiency. The proposed system offers a scalable and secure solution that enhances aviation safety, minimizes operational costs, and optimizes aircraft maintenance strategies.