Multi-Scale Cluster-Graph Convolution Network With Multi-Channel Residual Network for Intelligent Fault Diagnosis

Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty in extracting the weak features of the signal under variable load conditions, GCN is not suitable for deep neural network (DNN), and the receptive scale of GCN is unknown that limits the application of GCN in machine fault diagnosis. To address these issues, a multi-scale cluster-graph convolution neural network with multi-channel residual network (MR-MCGCN) is proposed for machine fault diagnosis in this article. First, multi-channel residual network (MCRN) is proposed for extracting the weak feature in the signal. Then, the finite graph data of signal and different scales are generated by the autoencoder (AE) graph generation layer. Finally, a multi-scale cluster-graph convolution neural network is proposed for achieving intelligent fault diagnosis. Also, the three different datasets are used for verifying the effectiveness of the proposed MR-MCGCN. The experimental results show that the proposed MR-MCGNN can achieve the highest diagnosis results than other methods even under variable load conditions.