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To address the problem of insufficient failure data generated by disks andthe imbalance between the number of normal and failure data. The existingConditional Tabular Generative Adversarial Networks (CTGAN) deep learningmethods have been proven to be effective in solving imbalance disk failuredata. But CTGAN cannot learn the internal information of disk failure data verywell. In this paper, a fault diagnosis method based on improved CTGAN, aclassifier for specific category discrimination is added and a discriminatorgenerate adversarial network based on residual network is proposed. We named itResidual Conditional Tabular Generative Adversarial Networks (RCTGAN). Firstly,to enhance the stability of system a residual network is utilized. RCTGAN usesa small amount of real failure data to synthesize fake fault data; Then, thesynthesized data is mixed with the real data to balance the amount of normaland failure data; Finally, four classifier (multilayer perceptron, supportvector machine, decision tree, random forest) models are trained using thebalanced data set, and the performance of the models is evaluated using G-mean.The experimental results show that the data synthesized by the RCTGAN canfurther improve the fault diagnosis accuracy of the classifier.

An improved CTGAN for data processing method of imbalanced disk failure