In few-shot fault diagnosis tasks in which the effective label samples are scarce, the existing semi-supervised learning (SSL)-based methods have obtained impressive results. However, in industry, some low-quality label samples are hidden in the collected dataset, which can cause a serious shift in model training and lead to the performance of SSL-based method degradation. To address this issue, the latest prototypical network-based SSL techniques are studied. However, most prototypical network-based scenarios consider that each sample has the same contribution to the class prototype, which ignores the impact of individual differences. This article proposes a new SSL method based on pseudo-labeling multi-screening for few-shot bearing fault diagnosis. In the proposed work, a pseudo-labeling multi-screening strategy is explored to accurately screen the pseudo-labeling for improving the generalization ability of the prototypical network. In addition, the AdaBoost adaptation-based weighted technique is employed to obtain accurate class prototypes by clustering multiple samples, improving the performance that deteriorated by low-quality samples. Specifically, the squeeze and excitation block technique is used to enhance the useful feature information and suppress non-useful feature information for extracting accuracy features. Finally, three well-known bearing datasets are selected to verify the effectiveness of the proposed method. The experiments illustrated that our method can receive better performance than that of the state-of-the-art methods.

Under small distribution discrepancies between source and target domains, the existing domain adaptation-based fault diagnosis methods can achieve encouraging results. However, in practical industrial applications, various machines and working conditions change unpredictably, significant inter-domain distribution discrepancies occur, seriously affecting the performance of domain adaptation-based methods. To tackle this problem, most of existing domain adaptation scenarios for fault diagnosis of different machines only focus on single-level domain adaptation, which cannot comprehensively consider discrepancy variations from the target domain. In this paper, a multi-levels domain alignment adaptation-based transfer fault diagnosis method for different machines was proposed, where the source domain and target domain originate from different machinery with significant distribution discrepancies. In the proposed method, intra-class spatial reconstitution is explored to excavate the potential category information hidden in the target domain for improving the performance and generalization of domain adaptation. Moreover, a multi-levels domain information alignment strategy is employed to address the shortcoming caused by single-level domain alignment that does not consider the influences of multi-levels distribution discrepancy fluctuation. Two well-known bearing datasets and a real industrial application dataset are used to verify the proposed method. The results show that the proposed method can achieve better performance than the SOTA methods.