AI RESEARCH
Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
arXiv CS.AI
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ArXi:2507.09766v2 Announce Type: replace-cross Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), ing 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