Remaining Useful Life Prediction for Turbofan Engines Based on Deep Learning Insights from the CMAPSS Dataset

2026-07-20T22:32:24+02:00

Predicting the Remaining Useful Life (RUL) of turbofan engines is a vital component in the field of prognostics and health management (PHM). Accurate estimation of RUL enables timely maintenance and prevents sudden failures, which is essential for ensuring safety and reducing operational costs. This study introduces a novel hybrid deep learning model referred to as RCBLA (Residual Convolutional Bidirectional Long Short-Term Memory with Attention), designed specifically to handle the temporal and sequential characteristics of engine sensor data. The model architecture combines residual convolutional layers to enhance feature extraction, Temporal Convolutional Networks (TCN) for capturing long-range dependencies, Bidirectional LSTM layers for understanding both past and future contexts, and an attention mechanism to focus on the most critical time steps. Layer normalization is applied to improve convergence and training stability. The proposed model is evaluated using the FD001 and FD003 subsets of the C-MAPSS dataset. A piecewise linear degradation model is adopted, and only sensor measurements are used as input features. A custom learning rate scheduler is applied to optimize model convergence, and a dropout rate of 0.7 and 0.5 is used to prevent overfitting. The results demonstrate the model’s strong predictive capabilities, achieving RMSE values of 14.389 and 12.46 for FD001 and FD003, respectively. The scoring function yields 279.23 and 282.56 for FD001 and FD003, respectively. These outcomes highlight the model’s robustness and suitability for real-world RUL prediction, offering a reliable framework for improving PHM systems in aerospace applications.

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Remaining Useful Life Prediction for Turbofan Engines Based on Deep Learning Insights from the CMAPSS Dataset