Self-Adaptive Physics Informed Neural Network for Paper Insulation Degree of Polymerization Prediction

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction.