An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis

Different from most of deep learning-based rotating machinery diagnosis methods, graph convolutional network based method can effectively mine relationship between nodes in the graph by feature aggregation and transformation. But the performance is limited to graph quality. Currently, edge connections of the graph are often established by calculating the feature similarity of single sensor data. To further improve graph quality, an improved multi-channel graph convolutional network (iMCGCN) for rotating machinery diagnosis is proposed in this paper. Multi-sensor data are used to construct graphs, where corresponding undirected k-nearest neighbor graphs (UK-NNGs) are constructed for each sensor data. A parallel graph data processing framework is designed to extract graph features from the constructed UK-NNGs. Then, an iMCGCN is constructed to learn graph features and achieve multi-channel feature fusion. Case studies are implemented to verify effectiveness of the proposed iMCGCN in learning health features for fault diagnosis.