Fault Diagnosis of Unbalanced Databased on Global Optimization GAN
In order to solve the problem of high misclassification rate caused by inaccurate deep learning feature extraction of un-balanced data,a fault diagnosis method based on global optimization generation countermeasure network was proposed.Firstly,the fault diagnosis results of automatic encoder decoding network and deep neural network were used to guide the training of gen-erator,which effectively avoided the problems of model collapse and gradient disappearance.Then,a two-stage discriminator is designed,in which a deep neural network fault diagnosis model was added as an additional discriminator.At the same time,the traditional discriminator was used to filter the unqualified fault samples.The generator and two discriminators were optimized al-ternately to improve the generating ability of the generator and the recognition ability of the discriminator.The experimental re-sults show that the proposed method can effectively improve the fault diagnosis accuracy of unbalanced samples.
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