Deep adaptively dynamic edge graph convolution network with attention weight and high-dimension affinity feature graph for rotating machinery fault diagnosis

Abstract The graph neural network (GNN) has emerged as an effective way to mine relationships between data due to its powerful modeling capability for graph structure data, and it has garnered significant attention from researchers for intelligent fault diagnosis tasks. However, the adjacency matrix of most GNN models with deep architecture is always fixed during the aggregation process, and the edge connection relationship cannot be adaptively adjusted, which limits their performance for feature representation. Moreover, for few-shot diagnosis scenarios, the generalization performance of deep GNN models will be further degraded due to fixed receptive fields and limited training samples. To address these issues, a deep adaptively dynamic edge graph convolution network (DADE-GCN) with attention weight and a high-dimension affinity feature graph is proposed. First, a deep adaptively dynamic edge graph convolutional module with attention weight (DADE-GCNWAW) is developed to dynamically adjust the receptive field in different graph convolution layers. Subsequently, the output features of different layers are fused by a self-attention mechanism. Second, to overcome the effect of the time-shift problem existing in vibration signals and capture accurate interdependencies between data, a high-dimension affinity feature graph construction method is proposed to construct graph structure data. The effectiveness of the proposed method is quantitatively verified by two rotating machinery datasets, which indicate that the proposed DADE-GCN model can achieve average diagnosis accuracies of 98.80% in both the two few-shot diagnosis tasks, which outperform several state-of-the-art recognition methods.