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In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model's performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification. [ABSTRACT FROM AUTHOR]

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model. [ABSTRACT FROM AUTHOR]