Toward accurate RUL and SOH estimation using reinforced graph-based PINNs enhanced with dynamic weights

• URL: http://arxiv.org/abs/2507.09766v1
• Date: Sun, 13 Jul 2025 19:49:12 GMT
• Title: Toward accurate RUL and SOH estimation using reinforced graph-based PINNs enhanced with dynamic weights
• Authors: Mohamadreza Akbari Pour, Ali Ghasemzadeh, MohamadAli Bijarchi, Mohammad Behshad Shafii,
• Abstract summary: We propose a framework that combines physics-based supervision with advanced-temporal learning.<n>Q-learning agents dynamically assign weights to physics-informed loss terms, improving generalization across real-time industrial systems.<n>In both RUL and SOH estimation tasks, the proposed method consistently outperforms state-of-the-art models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SOH) is essential for Prognostics and Health Management (PHM) across a wide range of industrial applications. We propose a novel framework -- Reinforced Graph-Based Physics-Informed Neural Networks Enhanced with Dynamic Weights (RGPD) -- that combines physics-based supervision with advanced spatio-temporal learning. Graph Convolutional Recurrent Networks (GCRNs) embed graph-convolutional filters within recurrent units to capture how node representations evolve over time. Graph Attention Convolution (GATConv) leverages a self-attention mechanism to compute learnable, edge-wise attention coefficients, dynamically weighting neighbor contributions for adaptive spatial aggregation. A Soft Actor-Critic (SAC) module is positioned between the Temporal Attention Unit (TAU) and GCRN to further improve the spatio-temporal learning. This module improves attention and prediction accuracy by dynamically scaling hidden representations to minimize noise and highlight informative features. To identify the most relevant physical constraints in each area, Q-learning agents dynamically assign weights to physics-informed loss terms, improving generalization across real-time industrial systems and reducing the need for manual tuning. In both RUL and SOH estimation tasks, the proposed method consistently outperforms state-of-the-art models, demonstrating strong robustness and predictive accuracy across varied degradation patterns across three diverse industrial benchmark datasets.

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