Conventional diagnostic methods that require a large amount of data support in practical engineering are difficult to effectively perform centrifugal pump fault diagnosis under small sample conditions. Therefore, the residual network (ResNet) in deep learning was combined with dilated convolution and extended into a siamese network to construct a dilated residual siamese network(DRSN). The dilated residual network was used as the feature extraction module of the siamese network, which enhanced the feature extraction ability of the model. Positive and negative sample pairs were constructed to extract more information from each sample, and make more effective use of limited data. The two sub-networks share parameters, the number of free parameters and lowering the risk of overfitting was reduced when the sample was insufficient. The proposed network model alleviated the problem of insufficient training samples, improved the efficiency of data utilization, and realized the fault classification of centrifugal pump under the condition of small samples. The research results show that even in the most sample-scarce situation, the accuracy of the model on the centrifugal pump test dataset can still reach 82.20%, which is at least 8.8 percentage points higher than other models.