A Novel Generative Model With Bounded-GAN for Reliability Classification of Gear Safety

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Reliability classification of gear safety has long been a challenging issue in transmission industry because of complicated calculations and great classification errors of coupled parameters with insufficient data. This paper proposes a model based on generative adversarial network (GAN) as pretreatment to improve the accuracy of reliability classification. First, we present bounded-GAN to generate gear data within required boundaries without massive computations. In bounded-GAN, three bounded layers are designed to bound generated data in terms of different data characteristics; smooth targets are developed to enhance the ability of generating high-quality instances by the generator; Adam optimizer is used to train both generator and discriminator to avoid nonconvergence. Second, to overcome unlabeling defect of bounded-GAN, a mean-covariance labeling scheme is introduced to label the data according to the nearest classes of gear reliability within specific ranges. Finally, original and qualified data are combined to train classifiers. Simulations on gear data from industry show that our proposed model outperforms other methods on operational metrics.

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IEEE Transactions on Industrial Electronics