Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines
2026-08-03 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a deep learning model to predict how much longer a combat aircraft engine can safely operate before needing maintenance. They used sensor data from NASA to teach the model to spot patterns of engine wear over time. Their model performed better than other common methods in predicting engine life and was effective even under different flight conditions. They also created a simulation tool to test their maintenance plan during intense flight missions. This helps improve engine reliability and reduce unexpected repairs.
remaining useful life (RUL)predictive maintenancedeep learningNASA C-MAPSS datasetsliding windowR-squaredRMSErisk scoreAUCsensor data
Authors
Fatih Ürgen, Doğay Altınel
Abstract
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model's architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.