GPSC-GAN: A Data Enhanced Model for Intelligent Fault Diagnosis

In manufacturing, accurate fault diagnosis is imperative but frequently impeded by the scarcity of data, which obstructs the development of effective data-driven diagnostic models. Although generative adversarial networks (GANs) are an effective means of increasing data volume, they still face a challenge in concurrently generating high-quality and multimode samples for multiple fault categories. To solve this challenge, a novel data enhanced model gradient penalty separate classifier (GPSC)-GAN based on GAN is proposed in this article, which is characterized by fault scenario-agnostic. First, a new separate classifier is developed to integrate into GAN to generate multimode fault samples. Second, Wasserstein distance with gradient penalty is introduced into the loss function of the discriminator to handle the optimization problem of the distribution similarity between generated samples and fault samples. Compared to the traditional GAN, the proposed model can more effectively produce generated samples that are aggregated with fault samples, which means that the generated samples are of high quality. Meanwhile, experimental results on two different bearing datasets reveal that the generated data by the proposed model are applicable to assist the training of deep learning-based fault diagnosis models with high accuracy and are also superior to the state-of-the-art models.