Fault diagnosis of centrifugal chillers based on CNN and ResNet

Convolutional neural network (CNN) is widely used in various fields due to its ability of autonomous learning and extracting features from a large amount of data, but it is rarely used in the field of refrigeration system fault diagnosis. In this paper, a fault diagnosis model for centrifugal chillers based on CNN is proposed, and the residual structure is used to optimize the model. The diagnosis results of seven typical faults of centrifugal chiller of ASHRAE RP-1043 project show that the overall fault diagnosis accuracy rate of ResNet_21 model with 21 convolutional layers is 99.40%, 7.48% higher than that of shallow network CNN_3. The refrigerant leakage fault diagnosis accuracy rate of system-level faults is 1.24% higher than that of CNN_14, reaching 98.55%. The identification of normal working conditions is more accurate, reaching 98.77%, and the false alarm rate is reduced by 1.43%. The diagnostic accuracy rate of local faults is above 99.7%.