Remaining useful life prediction of turbofan engines using long short-term memory networks

The accurate prediction of Remaining Useful Life for critical machinery is paramount for optimizing maintenance strategies and enhancing operational safety in industrial environments. This paper presents a robust RUL prediction model leveraging Long Short-Term Memory networks, a class of deep learning algorithms particularly well-suited for processing time-series data. The methodology encompasses comprehensive data preprocessing, including RUL labeling, normalization of sensor features, and sequence organization using sliding windows. The model architecture, consisting of multiple LSTM and dense layers, is detailed, along with its compilation using Mean Squared Error as the loss function and Adam as the optimizer. Key training strategies, such as adaptive learning rate scheduling and early stopping, are implemented to enhance performance and prevent overfitting. The model’s efficacy is rigorously evaluated using standard metrics like Root Mean Squared Error, S-score, R² score, and Explained Variance, demonstrating its capability in accurately forecasting the remaining operational lifespan of engines. The NASA Commercial Modular Aero-Propulsion System Simulation dataset is utilized as the primary input for RUL estimation, highlighting the model’s practical applicability in real-world prognostic and health management scenarios. Experimental results from cross-validation on the FD001 and FD003 subsets of the C-MAPSS dataset will be presented, validating the model’s predictive performance across consistent operational conditions and fault modes.