Rolling bearing fault diagnosis based on multi-scale weighted visibility graph and multi-channel graph convolution network

Current data-driven fault diagnosis methods are prone to overfitting and a decrease in accuracy when working with only a limited number of labeled samples. Additionally, existing graph neural network-based fault diagnosis methods often fail to comprehensively utilize both global and local features. To address these challenges, we propose a rolling bearing fault diagnosis method based on multi-scale weighted visibility graph and a multi-channel graph convolutional network (MCGCN). Our approach converts vibration signals into multiple weighted graphs from the perspective of geometric meaning and extracts local node feature information and global topology information of graphs using MCGCN. Experimental results demonstrate that our method achieves excellent performance under both sufficient and limited data conditions, providing a promising approach for real-world industrial bearing fault diagnosis.

Automated summary

This method proposes a fault diagnosis approach for rolling bearings, utilizing a multi-scale weighted visibility graph and a multi-channel graph convolutional network (MCGCN). By converting vibration signals into multiple weighted graphs and extracting both local node feature information and global topology information, our method achieves excellent performance under both sufficient and limited data conditions. It provides a promising approach for real-world industrial bearing fault diagnosis.