LAUSR

Several deep learning methods have emerged for fault diagnosis of industrial equipment in recent years. However, the realistic dataset is often much smaller than the benchmark diagnostic dataset due to the difficulty of fault data collection and labeling in realistic scenarios. Moreover, the collected fault data may come from various components with different fault categories. Therefore, existing deep-learning-based models have poor generalization capabilities for cases with only a few data when faced with new components. Herein, a triplet relation network (TRNet) is proposed for cross-component few-shot fault diagnosis by learning from several related meta-tasks iteratively. We construct a dual-channel feature embedding module with shared weights to extract fault features and a relation metric module to adaptively measure the feature similarity of sample pairs. Furthermore, in order to distinguish the most dissimilar samples in the same category (i.e., hard positive samples) and the most similar samples in different categories (i.e., hard negative samples), the hard sample recognition module is designed, combined with a triplet loss, to weaken the hard-to-discriminate feature of hard sample pairs, such that the TRNet are capable for task learning and feature learning to improve classification accuracy on target components. We conduct experiments on two publicly available datasets and one lab-built datasets. We validate the proposed method to classify with one, three, or five instances in each category of the target component. The results demonstrate that the fault diagnosis performance of our model is superior to the state-of-the-art methods.