Ss-infogan for class-imbalance classification of bearing faults

As the core part of the Prognostic and Health Management (PHM) of major equipment such as high-speed trains and aero engines, bearing fault classification have been the research priorities in the field. Although convolutional neural network (CNN) has shown good results in this type of task, the real application with limited training data makes CNN have a big gap between the actual application and the expected effect. Therefore, bearing faults classification with class-imbalance is a very practical work. In this paper, semi-supervised information maximizing generative adversarial network (ss-InfoGAN), which uses adversarial structure to generate samples of the minority, is introduced to augment data to solve class imbalance problem. In addition, the latent codes, the inputs of generator, are decomposed into three parts with three additional networks, respectively, at the start of generator. Meanwhile, the 50% precision threshold is proposed during the training stage of discriminator to make a trade-off between computing resources and theoretical foundations and facilitate the network converge. Bearing fault experiments are conducted to investigate the effectiveness of the presented network. The result shows classification accuracy is improved by 40% by the ss-InfoGAN compared to the traditional CNN for the case of extremely class-imbalance condition.