Asymmetric-Loss-Guided Hybrid CNN-BiLSTM-Attention Model for Industrial RUL Prediction with Interpretable Failure Heatmaps
Summary: arXiv:2604.13459v1 Announce Type: cross
Abstract
Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Useful Life (RUL) of critical components. Existing deep learning approaches frequently fail to simultaneously capture multi-sensor spatial correlations and long-range temporal dependencies, while standard symmetric loss functions inadequately penalize the safety-critical error of over-estimating residual life.
Model Overview
This study proposes a hybrid architecture integrating:
- Twin-Stage One-Dimensional Convolutional Neural Networks (1D-CNN)
- Bidirectional Long Short-Term Memory (BiLSTM) network
- Custom Bahdanau Additive Attention mechanism
The model was trained and evaluated on the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) FD001 sub-dataset employing a zero-leakage preprocessing pipeline, piecewise-linear RUL labeling capped at 130 cycles, and the NASA-specified asymmetric exponential loss function that disproportionately penalizes over-estimation to enforce industrial safety constraints.
Experimental Results
Experiments on 100 test engines achieved:
- Root Mean Squared Error (RMSE) of 17.52 cycles
- NASA S-Score of 922.06
Furthermore, extracted attention weight heatmaps provide interpretable, per-engine insights into the temporal progression of degradation, supporting informed maintenance decision-making.
Conclusion
The proposed framework demonstrates competitive performance against established baselines and offers a principled approach to safe, interpretable prognostics in industrial settings. This innovative model not only advances the field of predictive maintenance but also aligns with the critical need for safety in the aviation industry.