Spatial–Temporal Information Fusion Graph Convolutional Network for Bearing Remaining Useful Life Prediction

Graph convolutional networks (GCNs) can effectively learn graph data features and are widely used to predict the bearing remaining useful life (RUL). However, most existing GCN-based methods are based on single-node relationships to construct the graph structure while ignoring potential complex relationships between nodes. To enrich graph feature information and improve bearing RUL prediction accuracy, a novel bearing RUL prediction method is proposed in this article, which use spatial–temporal information fusion graph convolution network. The proposed method mines implicit temporal dependence and spatial correlation in graph samples by constructing PathGraph and RadiusGraph, which effectively improves the feature diversity of graph samples. In addition, to efficiently extract the spatial–temporal features of graph samples and alleviate the over-smoothing problem in GCNs, a feature extraction module combining graph attention network (GAT) and graph convolution network–temporal convolution network (GCN-TCN) is designed, where GAT dynamically assigns edge weights to capture spatial dependencies, while GCN-TCN enhances global temporal dependency capture. The experimental validation results show that the proposed method effectively improves bearing RUL prediction accuracy.