Transferable layered physics-informed learning for status sensing of high-power induction furnace

High-power induction furnace (IF) is a highly complex thermoelectric system with strong nonlinear time-varying characteristics. The lack of direct online measurement methods complicates status awareness, leading to apparent "black-box" behavior and sensing difficulties. We propose a transferable layered physics-informed learning-based modeling approach to address the above challenges. At the underlying level, a series of linear physical models are established at multiple operating points to guide the data-driven models, thus comprehensively capturing the data characteristics and electrical dynamics of the IF. At the upper level, physical prior knowledge-based global nonlinear constraints are introduced to ensure the model accuracy and physical consistency. Each underlying model can be regarded as a single task, and the modeling problem is skillfully transformed into a multitask learning optimization with global nonlinear constraints. In addition, a transferable model training strategy with an architecture of cascaded shared layers and task layers is developed to facilitate knowledge transfer between adjacent melting batches and thereby optimize the training process. The feasibility and effectiveness of the scheme are validated by experiments using actual sampling data.