Semi-supervised meta-path space extended graph convolution network for intelligent fault diagnosis of rotating machinery under time-varying speeds

In practical engineering scenarios, the operating speed of mechanical equipment is intricate and variable. However, much of the existing research on intelligent fault diagnosis is conducted under constant speed conditions, with limited studies focusing on fault diagnosis in the presence of time-varying speeds. Moreover, the limitation of labeled data poses considerable obstacles for intelligent fault diagnosis methodologies. Therefore, a semi-supervised meta-path space extended graph neural network (ME-GNN) is proposed for fault diagnosis in the context of time-varying speeds and limited labeled samples. Firstly, a novel heterogeneous graph is proposed, which converts the nearest neighbor relationship between vibration data, fault information and variable speed information into a graph. This kind of graph not only integrates diverse physical information but also facilitates message passing and aggregation across heterogeneous data types. To obtain the feature information of heterogeneous graphs from different feature space, meta-path space extended graph convolution network is implemented to aggregate information from different attribute nodes. Finally, the designed feature fusion module effectively integrates node features and topological information, thereby further expanding the feature space and enhancing the diagnostic capability of the model. A series of comparative experiments validate that the proposed method surpasses existing fault diagnosis methods.