SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples

Real industrial scenarios struggle with the issues of a limited number of labeled samples and difficulty in accessing, which results in deep learning-based fault diagnosis models having poor generalization capabilities and decreased diagnostic accuracy. To address this problem, a semi-supervised prototype enhancement network (SSPENet) is proposed for rolling bearing fault diagnosis in this study. Firstly, a dual pooling attention residual network is proposed to be used in the feature extraction module. The goal is to efficiently extract the hidden features within rolling bearings, thus enabling the accurate classification of different sample categories. Subsequently, the Hungarian algorithm is utilized to design a strategic approach to update prototypes with pseudo-labels, which achieves the effect of augmenting prototypes by accurately adjusting the prototype position of each class of limited labeled samples through unlabeled samples, to improve the discriminative ability of the network model for fault classes. Finally, validation and experimental analysis are carried out on two bearing datasets, which achieve the average diagnostic accuracy of the proposed model to be above 90 % for both 1-shot and 2-shot cases, obtaining more satisfactory diagnostic results.