AIBullisharXiv – CS AI · 7h ago6/10
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Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
Researchers present RGPD, a physics-informed neural network framework that dynamically balances multiple loss functions to improve Remaining Useful Life (RUL) and State of Health (SoH) predictions across industrial assets. The model achieves up to 20% improvement in accuracy over existing methods by combining graph-based representation learning with reinforcement learning-driven adaptive weighting, demonstrating strong generalization across engine, bearing, and battery degradation datasets.