Semi-supervised Few-shot Bearing Fault Diagnosis Method Based on Deep Multi-view Meta-learning

Few-shot learning (FSL) effectively addresses performance degradation caused by insufficient labeled data in the fault diagnosis of rotating machinery bearings. However, prevailing methods are confined to the contribution of the features from a single view, leaving unconsidered the information latent in other views capable of helping improve model accuracy. To address the issue of limited labeled samples in practical industrial processes, this paper proposes a deep multi-view meta-learning-based semi-supervised few-shot fault diagnosis method. In the proposed method, a deep multi-view feature fusion (MVFF) technique is explored to obtain more discriminative information features for training. Moreover, a feature layer self-attention variational autoencoder (FLSAVAE) is proposed and integrated into the feature extractor to pay attention to these hidden information contributed to model accuracy. Finally, a semi-supervised prototype refinement (SSPR) strategy is adopted to reduce the risk of low prototype accuracy in FSL, which is refined with the clustering centers of unlabeled data iteratively. Two well-known bearing datasets and a real industrial application dataset are selected to verify the proposed method. The results show that the proposed method can achieve better performance than SOTA methods.