To address the issues of mechanism deficiency and heavy data dependency in traditional high-voltage circuit breaker fault diagnosis models

a method for dynamic characteristic modeling and fault diagnosis of opening/closing coil currents based on a multiple-output physics-informed neural network is proposed. First

an electromechanical coupled mathematical model is constructed based on the operating mechanism of the opening/closing coils

and corresponding physical prior constraint functions are established. Then

considering the strong coupling relationship between coil current and the motion of the moving iron core

the differential equations are embedded into the corresponding branch networks in the form of residual loss. An adaptive weight adjustment strategy is introduced to balance the gradient flows among different loss terms

enabling fast and accurate forward inference and analysis of the dynamic characteristics of opening/closing currents. Finally

a fault diagnosis method integrated with transfer learning is proposed

allowing the prior-constrained model to be transferred to physical systems. Experimental results demonstrate that the proposed method achieves high physical consistency and solution accuracy

attaining a fault diagnosis accuracy of up to 96.86% even under limited measured data.