A Novel GAN-Based Signal Resolution Enhancement Method for Mechanical Fault Diagnosis Under Sample Insufficiency

The challenge of insufficient datasets is a significant issue in rotating machinery prediction and health management, particularly due to the scarcity of high-quality samples, which hampers the training of deep learning-based fault diagnosis models. generative adversarial networks (GANs) can effectively address the problem of insufficient samples. Therefore, in this article, a subpixel fully connected GAN (SPFCGAN) is proposed based on GANs to generate high-quality samples. Initially, the collected low-resolution (LR) signals are input into the generator of this network structure to produce high-resolution (HR) features through the fully connected layer and elemental rearrangement. The generated signals, along with the collected original HR signals, are then fed into the discriminator. By comparing the difference between the generated signals and the real signals, the discriminator performs signal discrimination, thereby improving the quality of the generated signal. The feasibility of the proposed SPFCGAN is validated by bearing data and gear data for fault diagnosis analysis.