ResNet50-3Cur-HGCN: a novel multimodal hybrid curvature space approach to bearing fault diagnosis

Abstract As a consequence of its great load-bearing capacity, low friction loss, and low vibration and impact, bearings are employed extensively in a variety of applications. In response to the current issues in the process of fault signal identification using graph neural networks (GNNs), such as the insufficient expression of node data types, the inadequate exploration of feature information carried by the graph structure, and the singularity of mathematical space operations on graph data, this paper proposes a residual network-50-heterogeneous graph convolutional neural network (ResNet50-HGCN) model based on the combination of ResNet50 and HGCN. The model is trained in a threefold mixed curvature (3 Curvature, 3Cur) space environment for semi-supervised learning, aiming to achieve accurate classification of bearing fault signals. Specifically, first, the time-domain signals and the components of the time–frequency map obtained from the wavelet synchrosqueezing transform are used as the bimodal node information for HGCN. Then, the graph network is merged in the 3Cur space for training and weighted validation, obtaining the predicted category labels of the model under the 3Cur configuration space. Finally, experimental data analysis is conducted, sparse confusion matrices for predicted categories are drawn, and four types of accuracy-related evaluation metrics are calculated. The experimental results show that the proposed ResNet50-3Cur-HGCN classification model outperforms other models in the experiment, achieving an accuracy of 97.71%, which verifies the method’s beneficial effects with regard to precision and efficiency. It also provides a good methodological reference for bearing fault diagnosis approaches centered on GNNs.