Application of Generative Adversarial Networks for Intelligent Fault Diagnosis

Fault diagnosis has attracted great attention on preventing the serious consequences from happening and guaranteeing the stability and reliability of machinery equipment. With the rapid development of artificial intelligence, Deep Learning (DL) based approaches begin to play great importance in the field of fault diagnosis. In this research, we proposed an image conversion pre-processing method to transform the time-domain signals of fault diagnosis into 2D images. And a designed structure of Generative Adversarial Networks (GAN) modeled by Convolutional Neural Network (CNN) is proposed to make the classification of fault. Datasets with different capacities are also experimented to study the performance of GAN on limited data. The results illustrate the potential of GAN on the small sample classification of fault diagnosis.