Abstract In the field of intelligent fault diagnosis, graph neural networks (GNNs) can create a richer fault feature space by modeling dependencies between sensor signals and embedding them in a structural attribute graph. However, existing GNNs models typically use monitoring signals to directly construct graphical datasets, which suffer from poor representation and edge redundancy.