Do RUL explanations hold up? Faithfulness and stability of attributions on C-MAPSS
This study addresses the limitations of existing explanation methods for deep remaining useful life (RUL) prediction models in terms of faithfulness and stability evaluation. For the first time, it systematically evaluates deletion/insertion faithfulness and cross-seed stability, explicitly distinguishing noise robustness from retraining consistency. Based on CNN, LSTM, and Transformer architectures, this work comparatively analyzes the explainable AI (XAI) performance of attribution algorithms, including Integrated Gradients, Occlusion, and attention mechanisms. The results demonstrate that Occlusion and Integrated Gradients achieve superior faithfulness, rendering them suitable for practical engineering maintenance reports, whereas raw attention should serve solely as a visualization aid. These findings provide a reliable foundation for interpretable predictive maintenance.