It is difficult to obtain enough samples for fault diagnosis of refrigeration equipment, which limits the performance of fault diagnosis models. To alleviate this problem, existing fault diagnosis algorithms for refrigeration systems first augment data with the data enhancement technology, then utilize the data filtering algorithm to filter low-quality samples and finally predict the fault types,. During the entire process, it is necessary to manually set appropriate thresholds for filtering samples, which is not applicable to automated industrial production. Moreover, existing methods only learn class information from augmented data, which neglect learning the relationship between samples of the same class. To alleviate the manual intervention problem while preserve the high accuracy of the fault diagnosis model, a novel fault diagnosis approach based on data augmentation and class feature aggregation model is proposed, which simultaneously implements data enhancement and fault diagnosis. In addition, a class feature aggregation module is embedded into the fault diagnosis model to ensure that sample features of the same class are close to each other, thereby improving the accuracy of fault prediction. The experimental results show that this method can be well applied to fault diagnosis of refrigeration equipment.