175 / 2024-09-01 11:19:54

Data synthesis using dual discriminator conditional generative adversarial networks

Rolling bearings, Fault diagnosis, Generative adversarial network.

Special Sessions > SS03 Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries

Diagnosis of rolling bearings plays an important role in condition monitoring of industrial rotating machinery. In many actual applications, rolling bearings work in normal state at most time and faulty samples are difficult to be collected. Thus, it is easy to arise problem of imbalanced dataset which restricts accuracy and stability of fault diagnosis. Generative adversarial networks (GANs) have been proved to be effective to produce artificial data that are alike real data, and have been widely used in image fields. Data synthesis using deep generative model provide a promising methodology for imbalanced fault diagnosis of machinery. In this paper, we propose a novel framework named dual discriminator conditional generative adversarial networks (D2CGANs) to learn from sensor signals on multimodal fault samples and automatically synthesize realistic one-dimensional signals of each fault.