Rotating Machinery Fault Diagnosis Based on Multi-sensor Information Fusion Using Graph Attention Network

Multi-sensor information acquisition system can reflect the operation status of machinery more comprehensively and reliably, but also demands higher requirements on data analysis algorithms. Unlike previous deep learning models, the emerging Graph Neural Network (GNN) has a remarkable performance in mining graph structure and patterns, effectively integrating multiple node relationships and features. This paper presents a fault diagnosis algorithm based on multi-sensor information fusion using the modified Graph Attention Network-GATv2. Firstly, the dependencies between multi-sensor signals are explicitly extracted by the Grow-Shrink (GS) algorithm, where the topology of the constructed graph can characterize different failure states of the equipment. During the aggregation process, the attention mechanism in the GATv2 assigns higher weights to informative nodes for the effective fusion of multi-sensor information. Experiments show that the proposed diagnosis framework can yield more expressive multi-sensor representations, and the diagnostic accuracy is improved significantly compared to the single-sensor graph.