Multi-GAT:基于多度量衡构建图的故障诊断方法OA北大核心CSTPCD

Fault diagnosis methods based on graph neural networks usually require determining the correlation between samples based on a metric,which in turn constructs the topology of the graph.However,the correlation between data samples may not be accurately measured based on a single metric,which in turn may not accurately reflect the relationship between samples.Therefore,the choice of different metrics can greatly affect the diagnostic performance of graph neural networks.In order to solve the problem that the correlation between data samples cannot be accurately characterized by a single metric,a fault diagnosis model,the multi-metrics graph attention network(Multi-GAT),is proposed to construct graphs based on multiple metrics.The strength of correlation between data samples is determined by combining the results of the three metrics.The scoring function of the graph attention network is improved to determine the similarity between data samples more accurately based on the strength of correlation between the samples.Experiments on a benchmark dataset show that Multi-GAT is able to improve the diagnostic accuracy of the model and has good stability.