Uncertainty-based contrastive prototype-matching network towards cross-domain fault diagnosis with small data

Deep learning-based fault diagnosis methods have to be trained by a large amount of labeled data for accurate diagnosis results. However, in real-case industrial scenarios, the data available is limited since the difficulty of obtaining fault samples. In addition, the monitoring data always come from various working conditions. As a result, data scarcity and domain shift have become two main factors that limit the performance of diagnosis models. We called this problem the few-shot cross-domain fault diagnosis, which has been rarely explored. To deal with the above challenge, we propose a novel uncertainty-based contrastive prototype-matching network (UCPMnet). Specifically, two main parts are conducted for UCPMnet. Considering the construction of representation spaces for compact intra-class and sparse inter-class embeddings, the contrastive prototypical network (CPN) is established by remolding the prototypical network. Subsequently, an uncertainty-based prototype-matching (UPM) method is presented for class-wise domain adaptation based on pseudo-label learning. At the same time, an estimation approach of prediction uncertainty is introduced to adaptively rectify the threshold for screening the pseudo label, which aims to mitigate the effect of noisy predictions for prototype-matching. Extensive experiments are conducted on two datasets, and four classical methods are selected for comparison. The results corroborate the superiority of the UCPMnet for fault diagnosis in the case of small data.