This paper proposes a novel fault diagnosis approach based on generative
adversarial networks (GAN) for imbalanced industrial time series where normal
samples are much larger than failure cases. We combine a well-designed feature
extractor with GAN to help train the whole network. Aimed at obtaining data
distribution and hidden pattern in both original distinguishing features and
latent space, the encoder-decoder-encoder three-sub-network is employed in GAN,
based on Deep Convolution Generative Adversarial Networks (DCGAN) but without
Tanh activation layer and only trained on normal samples. In order to verify
the validity and feasibility of our approach, we test it on rolling bearing
data from Case Western Reserve University and further verify it on data
collected from our laboratory. The results show that our proposed approach can
achieve excellent performance in detecting faulty by outputting much larger
evaluation scores