基于多判别器辅助分类器生成对抗网络的故障诊断方法研究OA北大核心CSTPCD

In extremely harsh working environments such as strong impacts,intense radiation and extremely high temperature,the fault modes of mechanical equipment are complex and varied,and it is very difficult to obtain sufficient and effective fault data,even difficult to achieve,so that the accuracy of fault diagnosis is limited,and subsequent maintenance and repair programs are difficult to be effectively developed.To solve this problem,a data enhancement algorithm for multi-discriminator auxiliary classifier generative adversarial network was proposed.By setting up 3 discriminators,1 generator and adding independent classifier,a new auxiliary classifier generative adversarial network model was constructed.Aiming at the instability issue in the model's training,the Wasserstein distance was introduced to construct a new loss function,and the unilateral soft constraint regularization term with more stability was used to replace the original L2 gradient penalty term to solve the problem of model collapse.Building on this,an efficient channel attention mechanism was adopted to further improve the model's feature extraction capability.The proposed model was applied to extend the fault data set of mechanical equipment to assist the training of deep learning intelligent diagnosis model.Multiple fault data set expansion experiments showed that compared with the existing model,the new model could generate higher quality data,and the accuracy of fault diagnosis was further improved,so it had high application value.