Due to the existence of dataset shifts, the distributions of data acquired from different working conditions show significant differences in real-world industrial applications, which leads to performance degradation of traditional machine learning methods. This work provides a framework that combines supervised domain adaptation with prototype learning for fault diagnosis. The main idea of domain adaptation is to apply the Siamese architecture to learn a latent space where the mapped features are inter-class separable and intra-class similar for both source and target domains. Moreover, the prototypical layer utilizes the features from Siamese architecture to learn prototype representations of each class. This supervised method is attractive because it needs very few labeled target samples. Moreover, it can be further extended to address the problem when the classes from the source and target domains are not completely overlapping. The model must generalize to unseen classes in the source dataset, given only a few examples of each new target class. Experimental results, on the Case Western Reserve University bearing dataset, show the effectiveness of the proposed framework. With increasing target samples in training, the model quickly converges with high classification accuracy.