A temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery

In the actual operation, rotating machinery mostly works under normal condition. The collected monitoring data often exhibit serious distribution imbalance with far more normal label samples than fault label samples, leading to poor recognition performance of standard intelligent diagnosis models. Besides, many intelligent diagnosis models rely on data generation to overcome this problem, which is subject to data generation differences. Therefore, to address above limitations, a novel temporal-spatial multi-order weighted graph convolution network (TSMOW-GCN) with refined feature topology graph is proposed. First, a multi-order weight graph convolution layer is proposed to aggregate multi-order weighted mixing neighbor information in different distances, which achieves broader representation and mines more features and relationships without data generation and deep network structure. Further, the feature modeling in temporal dimensions is considered. Second, a refined feature topology graph construction method is developed to obtain compact and efficient feature topology graphs, which can improve the ability of graph representation. Besides, a dynamically adjusted label smoothing regularization loss is proposed to further improve generalization ability and avoid overfitting of the trained model under imbalance data. Two rotating machinery datasets are used to quantitatively verify proposed method, indicating that the TSMOW-GCN outperforms several advanced approaches under various imbalance ratios.