Abstract: This project aims to develop a predictive maintenance system for aircraft engines using machine learning techniques. Leveraging historical maintenance records and real-time sensor data, the system will predict engine failures and maintenance requirements, optimizing maintenance schedules and reducing downtime. Key steps include data collection, preprocessing, feature engineering, model selection, training, and deployment. The project seeks to improve aircraft safety, operational efficiency, and cost-effectiveness by enabling proactive maintenance interventions based on predictive insights. Through continuous monitoring and updating, the system will adapt to evolving operational conditions, ensuring reliable performance and minimizing the risk of unexpected engine failures.

Keywords: Predictive maintenance, Aircraft engines, Machine learning, Engine failure prediction Real-time sensor data Maintenance schedules Operational efficiency, Proactive maintenance

| DOI: 10.17148/IARJSET.2024.11732