Oversampling adversarial network for class-imbalanced fault diagnosis

The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for a mixed type of data or while there is overlapping between classes. Classimbalance prob-lem requires a robust learning system which can timely predict and classify the data. We propose a new adversarial network for simultaneous classification and fault detection. In particular, we restore the balance in the imbalanced dataset by generating faulty samples from the proposed mixture of data distribution. We designed the discriminator of our model to handle the generated faulty samples to prevent outlier and overfitting. We empirically demonstrate that; (i) the discriminator trained with a generator to generates samples from a mixture of normal and faulty data distribution which can be considered as a fault detector; (ii), the quality of the generated faulty samples outperforms the other synthetic resampling techniques. Experimental results show that the proposed model performs well when comparing to other fault diagnosis methods across several evaluation metrics; in particular, coalescing of generative adversarial network (GAN) and feature matching function is effective at recognizing faulty samples. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces a new adversarial network model for simultaneous classification and fault detection. By generating faulty samples from a mixture of data distribution to restore balance in imbalanced datasets, the proposed model performs well in experiments, particularly in recognizing faulty samples.