Few-shot bearing fault diagnosis based on ResAttn-Siamese

Few-shot bearing fault diagnosis is defined as the process of identifying bearing faults utilizing only a limited set of labeled samples. Predominantly, Siamese networks, Prototypical networks, and Matching networks are the methods extensively employed in the realm of few-sample bearing fault diagnosis. However, these prevailing methodologies often neglect the crucial aspect of feature representation learning and overlook the key characteristics inherent in bearing fault samples. To address this issue, we propose a novel Siamese network model, dubbed ResAttn-Siamese, which integrates a residual network with a self-attention mechanism to enhance both the performance and effectiveness of the network. Initially, we utilize a pre-trained ResNet model as the feature extractor within the Siamese network. Subsequently, a self-attention mechanism is applied to the ResNet-extracted features. Feature fusion is achieved by amalgamating the ResNet-derived feature representation with the weighted feature representation produced by the self-attention mechanism. The entire Siamese network undergoes training using the bearing fault diagnosis dataset. Upon completion of training, the network is thoroughly evaluated and tuned as necessary. This comprehensive process enhances the network's ability to learn distinctive feature representations of bearing fault samples, thereby augmenting both the accuracy and robustness of the fault diagnosis.