LAUSR

Abstract Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical equipment operation. Recent works show that deep learning-based methods outperform most of traditional fault diagnosis techniques. However, a practical problem comes up in these studies, where deep learning models cannot be well trained and the classification accuracy is greatly affected because of the sample-imbalance problem, which means that there are a large amount of normal data but few fault samples. To solve the problem, an enhanced generative adversarial network (E-GAN) is proposed. Firstly, the deep convolutional generative adversarial network (DCGAN) is utilized to generate more samples to balance the training set. Then, by integrating K-means clustering algorithm, we developed a modified CNN diagnosis model for fault classification. The experiment results demonstrate that the proposed E-GAN can greatly improve the classification accuracy and is superior to the compared methods.