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.
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.
@cite_3
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Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5 over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.