Fault Diagnosis of Gearbox Based on Refined Topology and Spatio-Temporal Graph Convolutional Network

A gearbox is an indispensable component of a mechanical transmission system, and in the event of an anomaly, it can have a serious impact on the stability of the system. Graph convolutional network (GCN) have achieved some results in the study of fault diagnosis, but the way of topology construction from a single metric space is somewhat inexact and one-sided. Meanwhile, GCNs can only aggregate node features in the spatial dimension, which does not guarantee a comprehensive expression of fault information. Therefore, we propose a gearbox fault diagnosis method based on refined topology and spatio-temporal GCN to accurately identify the gearbox health state. First, two topologies are constructed based on the distance metric and the similarity metric, respectively, and the edge structure is pruned to obtain a more lightweight topology based on the analysis of the structural similarity between the two. Then, depending on the number of common neighbors of two nodes, the weights between them are enhanced or weakened to further refine the topological information. Finally, a network model with spatio-temporal feature co-extraction is developed to mine fault features in depth in two dimensions to improve network performance. Case studies, ablation experiments, and robustness validation of the proposed method are carried out, and the results show that the accuracy of the proposed method is as high as 99.41% and 99.93% on the two datasets, respectively, which is much higher than that of other comparison methods, and at the same time, it has good noise robustness.