To address the challenge of fault diagnosis for asynchronous motors under strong interference

multi-sensor fusion can improve accuracy

but effective fusion of heterogeneous sensor data and utilization of inter-sensor correlations and physical prior knowledge remain difficult problems.An AC motor fault diagnosis method based on heterogeneous multi-sensor fusion with graph neural network(GNN)is proposed.A multi-task enhanced autoencoder is employed to extract node features to learn discriminative representations; an adjacency matrix incorporating physical prior constraints is constructed to enhance generalization; and a graph isomorphism network(GIN)is used to obtain graph-level representations for classification.Experimental results show that the proposed model outperforms traditional methods in fault diagnosis performance on two heterogeneous asynchronous motor datasets.The conclusion confirms that the proposed framework significantly improves the accuracy and robustness of fault diagnosis through effective data fusion and integration of physical knowledge.