Toward accurate remaining useful life and state of health estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
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 fixe...