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The collected data from industrial machines are often imbalanced, which posesa negative effect on learning algorithms. However, this problem becomes morechallenging for a mixed type of data or while there is overlapping betweenclasses. Class-imbalance problem requires a robust learning system which cantimely predict and classify the data. We propose a new adversarial network forsimultaneous classification and fault detection. In particular, we restore thebalance in the imbalanced dataset by generating faulty samples from theproposed mixture of data distribution. We designed the discriminator of ourmodel to handle the generated faulty samples to prevent outlier andoverfitting. We empirically demonstrate that; (i) the discriminator trainedwith a generator to generates samples from a mixture of normal and faulty datadistribution which can be considered as a fault detector; (ii), the quality ofthe generated faulty samples outperforms the other synthetic resamplingtechniques. Experimental results show that the proposed model performs wellwhen comparing to other fault diagnosis methods across several evaluationmetrics; in particular, coalescing of generative adversarial network (GAN) andfeature matching function is effective at recognizing faulty samples.

Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis