Fault diagnosis model of refrigerant leakage in evaporators based on physics-guided neural network

Refrigerant leakage is a common problem in refrigeration and air conditioning systems, and the current mainstream data-driven diagnostic methods generally have defects such as insufficient physics-informed guidance, high dependence on data, and poor extrapolation performance. In this paper, an evaporator refrigerant leakage fault diagnosis model based on physics-guided neural network (PGNN) is proposed, in which the relevant features are selected according to the energy conservation equation of the heat exchange process, and the physical loss function is incorporated into the neural network to better capture the physical changes caused by refrigerant leakage. The experimental results show that the PGNN model achieves average accuracy rates of 99.73% and 99.70% on the training and testing datasets, with the best results reaching up to 100.00%. In contrast, the traditional multi-layer perceptron (MLP) model only achieves an accuracy rate of 81.74% and has a high false alarm rate of 30.89%. When the proposed method is applied to the LightGBM and SVM models, the accuracy rates are increased from 98.31%and 81.01% to 99.58% and 94.09%, respectively, indicating that the proposed method has good extrapolation performance.