基于动态权重的强化图物理信息神经网络实现精确的剩余使用寿命和健康状态估计
Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
AI总结：
提出一种结合图表示学习、强化学习和自适应动态权重的物理信息神经网络框架RGPD，在C-MAPSS、PHM2012和XJTU数据集上实现跨资产退化场景的RUL和SoH高精度估计。
摘要：
精确估计剩余使用寿命（RUL）和健康状态（SoH）对于可靠的预测与健康管理（PHM）至关重要，有助于及时维护和可靠的工业运行。然而，结合数据驱动学习与基于物理的正则化的混合模型通常依赖于固定的损失权重，因此在跨具有不同退化行为的资产迁移时会失去准确性。本研究引入了具有动态加权的强化图物理信息网络（RGPD），这是一个用于时空退化建模和自适应物理引导正则化的统一框架。基于图的表示学习捕获传感器间的退化结构，软演员-评论家（SAC）模块在噪声条件下细化潜在特征，轻量级Q学习策略在训练过程中自适应地平衡单调性、平滑性和潜在动力学残差损失。该框架在C-MAPSS、PHM2012和XJTU数据集上进行了评估，这些数据集分别代表发动机、轴承和电池的退化过程。与相应基准表中报告的最强基线相比，RGPD在PHM2012和C-MAPSS上将平均RMSE提高了高达12%，在XJTU上将平均MAPE比第二好的模型降低了20%。在这些异构基准上的性能进一步表明了该模型跨退化系统的泛化能力。物理信息组件通过退化一致性先验以及深度隐藏物理模型风格的残差实现，提高了物理合理性，而无需为每种资产类型建立完整的第一性原理模型。
英文摘要：
Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation. However, hybrid models that combine data-driven learning with physics-based regularization often rely on fixed loss weights and therefore lose accuracy when transferred across assets with different degradation behaviors. This study introduces Reinforced Graph-based Physics-informed Networks with Dynamic Weighting (RGPD), a unified framework for spatio-temporal degradation modeling and adaptive physics-guided regularization. Graph-based representation learning captures inter-sensor degradation structure, a Soft Actor-Critic (SAC) module refines latent features under noisy conditions, and a lightweight Q-learning policy adaptively balances monotonicity, smoothness, and latent-dynamics residual losses during training. The framework is evaluated on the C-MAPSS, PHM2012, and XJTU datasets, which represent engine, bearing, and battery degradation processes. Relative to the strongest compared baselines reported in the corresponding benchmark tables, RGPD improves average RMSE by up to 12 percent on PHM2012 and C-MAPSS, and reduces average MAPE by 20 percent on XJTU compared with the second-best reported model. Performance on these heterogeneous benchmarks further suggests the model's generalizability across degradation systems. The physics-informed component is implemented through degradation-consistent priors together with a Deep Hidden Physics Model-style residual, which improves physical plausibility without requiring a full first-principles model for each asset type.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
