Enhanced generative adversarial networks for fault diagnosis of rotating machinery with imbalanced data

Deep learning-based methods have attracted the attention of researchers due to their outstanding performance in automatic feature learning, a crucial step for satisfactory fault diagnosis. However, faults in rotating machinery may occur occasionally, and fault-related signals are difficult to collect, resulting in imbalanced data. This problem is a major concern in fault diagnosis research. In this study, an enhanced generative adversarial network (EGAN) is proposed to establish a fault diagnosis model for rotating machinery. The model, based on a 2D convolutional network, consists of a generator and a discriminator. The generator produces specified samples to automatically enrich small samples for balancing datasets. The discriminator validates the distribution similarity between the generated and original samples. Fault types are recognized by a classifier using the generated and original samples. An adaptive training ratio strategy is also proposed to improve the convergence rapidity and stability of the EGAN training process. In case studies, two datasets are applied to verify the generalization performance of the EGAN. Results confirm the superior performance of the EGAN in fault diagnosis, particularly in the analyses of datasets with imbalanced data. Moreover, visualization results demonstrate that the proposed EGAN exhibits satisfactory ability and great potential for fault diagnosis applications of rotating machinery.