GAN-based Bearing Fault Diagnosis Method for Short and Imbalanced Vibration Signal

Bearings are vital components of a rotary machine. Real-time fault diagnosis of bearings has great significance in the maintenance of equipment. Current vibration-based fault diagnosis methods rely on the usage of long time series data to reduce the influence of noise but often suffer from imbalances in datasets, significantly affecting diagnosis accuracy. In this paper, a fault diagnosis method based on Intertemporal Return Plot and data augmentation is proposed. The Intertemporal Return Plot is employed to transform one-dimensional time series data into two-dimensional images. The Wasserstein Generative Adversarial Network is employed to generate synthetic images for data argumentation. This approach helps reduce the influence of data imbalance, thereby improving the accuracy and convergence speed of the fault diagnosis. Comparisons with existing well-known methods are performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method achieves a high accuracy of fault diagnosis for short time series data. An inverse transformation is also proposed to convert the images to time series data.