Securing Aircraft Maintenance Data with Predictive Insights

Abstract

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

Introduction

Aircraft maintenance is crucial for aviation safety but traditional methods relying on manual records and scheduled inspections face issues such as inefficiency, data tampering, and delayed fault detection. This paper proposes an advanced maintenance system integrating blockchain and machine learning to improve security, transparency, and predictive maintenance.

• Blockchain provides an immutable, tamper-proof ledger for maintenance records, ensuring data integrity and compliance, while enabling secure data sharing among stakeholders.

• Machine learning, particularly LSTM models, analyzes real-time sensor data to predict the Remaining Useful Life (RUL) of aircraft components, allowing proactive maintenance scheduling and reducing unplanned downtime.

• Smart contracts automate validation processes and maintenance alerts, reducing manual intervention and streamlining regulatory compliance.

The system design includes a blockchain layer for secure record-keeping and a machine learning layer for predictive analytics. The implementation uses Ethereum smart contracts, LSTM for predictive models, and a web interface for user interaction. The paper details the system modules, data structures, and stepwise LSTM model training and evaluation.

Testing focuses on validating individual modules (unit testing), ensuring smooth integration (integration testing), and measuring performance metrics like response time and scalability. Expected results include enhanced data security, improved prediction accuracy for maintenance needs, and reduced operational costs through minimized unplanned downtime.

Conclusion

This project demonstrates a robust approach to securing aircraft maintenance data and optimizing maintenance schedules using blockchain and machine learning. By leveraging blockchain technology, maintenance records remain immutable and transparent, reducing the risk of data manipulation and enhancing trust among stakeholders. Meanwhile, machine learning models provide accurate predictions of engine failures, enabling proactive maintenance strategies that minimize unexpected breakdowns and improve operational efficiency. The integration of these technologies significantly improves aviation safety, reduces costs, and ensures regulatory compliance. Future work includes expanding predictive capabilities using deep learning techniques, integrating AI-driven diagnostics for real-time anomaly detection, and enhancing system scalability for deployment across various aircraft models. Furthermore, incorporating more extensive datasets and advanced security mechanisms will enhance the model’s accuracy and robustness. This research serves as a foundation for the next generation of intelligent aviation maintenance systems.

References

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Copyright

Copyright © 2025 Prof. Shilpa T, Agam Agarwal, Aryan Singh, Aryan Dhariwal, Prof. Vedasree T K, Aayush Prakash. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.