Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
- URL: http://arxiv.org/abs/2008.03071v1
- Date: Fri, 7 Aug 2020 10:12:07 GMT
- Title: Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
- Authors: Masoumeh Zareapoor, Pourya Shamsolmoali, Jie Yang
- Abstract summary: Class-imbalance problem 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.
- Score: 12.526197448825968
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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. Class-imbalance problem 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.
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