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.
@cite_3: Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.
As can be seen from above, more recent attention in the literature has been focused on the provision of adversarial training, especially on GAN. GAN, viewed as an unsupervised machine learning algorithm, since initially introduced by in 2014, has achieved outstanding application effects in the field of image recognition. Based on GAN, there has been emerged various kinds of adversarial algorithm. For further details, we refer the interested reader to a website which gives a very comprehensive summary of GAN and its variants [website: https: github.com hindupuravinash the-gan-zoo]. Last year, based on GAN, a generic anomaly detection architecture called GANomaly put forward by in @cite_3 shows superiority and efficacy compared with previous state-of-the-art approaches over several benchmark image datasets, which gives us an inspiration for fault diagnosis in industrial area. To explain our approach thoroughly in next part, we will briefly introduce GANomaly.