International Journal of Scientific Research and Engineering Development

Turbofan engines operate under extreme thermal, mechanical, and environmental stresses, making timely maintenance critical for safety, reliability, and cost efficiency. Traditional maintenance strategies such as reactive and fixed-interval preventive maintenance often lead to unexpected failures or unnecessary component replacements. Remaining Useful Life (RUL) prediction introduces a predictive-maintenance framework that estimates the time or operational cycles left before an engine component reaches its failure threshold. By leveraging historical sensor data, operational parameters, and degradation patterns, machine learning and deep learning models can forecast engine health with high accuracy. This approach integrates data preprocessing, feature extraction, and advanced predictive algorithms such as Long Short-Term Memory networks, Random Forests, and Support Vector Regression to generate accurate RUL estimates. These predictions are then combined with optimization techniques to schedule maintenance activities at the most cost-effective and risk-aware time. The methodology reduces unscheduled downtime, enhances fleet availability, and improves operational safety while minimizing maintenance costs. Despite challenges such as data imbalance, sensor noise, and real-time deployment constraints, RUL-based maintenance optimization represents a transformative shift toward intelligent, data-driven aviation asset management.

Rakshitha S, Mrs.N.Vaishnavi,"Optimizing Maintenance Schedules of Turbofan Engines Using Remaining Useful Life ( RUL) Prediction" International Journal of Scientific Research and Engineering Development, V9(1): Page(1937-1941) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.