• Nirmalkumar S. Benni
• Prerana Ravikiran
• Ronit Suresh
• Ruchir Rohan Trimurthy
• Uttam K.S

The Advanced Aircraft Engine Failure Prediction System aims to improve aviation safety and operational efficiency through advanced machine learning techniques. By analyzing both real-time and historical sensor data—including parameters such as temperature, pressure, vibration, and fuel flow—the project identifies early indicators of engine degradation. This study compares the performance of various machine learning algorithms, focusing on classification and regression models, to accurately forecast the remaining useful life (RUL) of engine components. Through a detailed assessment of model accuracy, reliability, and efficiency, this project provides insights into the most effective strategies for predictive maintenance. The findings support the optimisation of maintenance schedules, reduction of aircraft downtime, and minimization of operational costs, ultimately advancing proactive, data-driven maintenance solutions that ensure a safer and more cost-efficient aircraft operations.

Predictive Maintenance, Machine Learning Models, Aircraft Engine Failure, Sensor Data Analysis, Remaining Useful Life (RUL), Classification and Regression Models, Operational Efficiency, Real-time Monitoring, Engine Degradation Indicators, Proactive Maintenance Solutions

"Advanced Aircraft Engine Failure Prediction System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.c453-c456, December-2024, Available :http://www.jetir.org/papers/JETIR2412252.pdf

"Advanced Aircraft Engine Failure Prediction System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppc453-c456, December-2024, Available at : http://www.jetir.org/papers/JETIR2412252.pdf