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