Graph features dynamic fusion learning driven by multi-head attention for large rotating machinery fault diagnosis with multi-sensor data

Recently, rotating machinery fault diagnosis studies based on graph neural networks (GNN) have received some satisfactory achievements. But most of them are based on the analysis of the single sensor signals, which cannot capture the comprehensive fault information, especially aiming at large rotating machineries. A few research using GNN for multi-sensor fault diagnosis only fuse multi-source features in the construction of the input graph, and the fusion effect largely depends on the manual feature selection. Graph attention network (GAT), as an emerging GNN, can give trainable weights to vertices based on the self-attention mechanism to improve the effectiveness of feature learning. And it has not yet been used in the field of multi-sensor fault diagnosis. To fill this gap and utilize GAT's advantages, this paper presents a multi-sensor multi-head GAT (MMHGAT) model for large rotating machinery fault diagnosis. With the input of several subgraphs, the designed MMHGAT model consisting of two graph attention layers (GAL), a feature fusion process and a Softmax classifier, can dynamically fuse and mine the high-level fault characteristics during the training process. By employing the experiment on the axial flow pump, the effectiveness and superiority of the proposed method are validated.

Automated summary

This paper proposes a multi-sensor multi-head GAT model for fault diagnosis of large rotating machinery, which can dynamically fuse and mine high-level fault characteristics to improve the effectiveness of diagnosis.