变工况下动态卷积域对抗图神经网络故障诊断OA北大核心CSTPCD

This paper proposes a dynamic convolutional domain adversarial graph neural network fault diagnosis model based on unsupervised domain adaptation theory.Traditional domain alignment methods,such as the average maximum difference method,often ignore inter-domain data structure information and exhibit poor global generaliza-tion ability.This model addresses these issues by separately modeling the category labels,domain labels,and data structure information.The approach embeds data structure information into instance graph nodes using a graph neu-ral network.Category labels and domain labels are modeled using classifiers and domain discriminators,respective-ly.The Gaussian Wasserstein distance is used to measure the differences between instance graphs in different do-mains.This paper compares the accuracy of fault identification across 14 migration tasks using various models and under different operating conditions.Experimental results show that the domain adversarial graph neural network model,based on dynamic convolution,outperforms other comparative models in fault diagnosis under varying oper-ating conditions and maintains stable performance.