Fault Diagnosis with Extremely Limited Samples: A Generalization Generator Improves GAN-Based Methods

Recently, generative adversarial networks (GANs) have provided a powerful data augmentation means for fault diagnosis in limited samples scenarios. Nevertheless, the challenge of poor generalization ability with extremely limited training data still limits effective applications of GANs-based methods for industrial fault diagnosis. To address this challenge, a novel generalization generator is proposed for machinery fault diagnostics under extremely limited samples. It mainly consists of a random sampling module, a generating module and a generalization module to improve the quality of generated samples. Meanwhile, our proposed generalization generator can be flexibly combined with the architecture of GANs, which proves its universality and robustness. A case study for machinery fault diagnostics considering extremely limited samples has been carried out to validate the effectiveness and superiority of our proposed method. Experiment results have demonstrated that our proposed generalization generator can generate high-quality time-series samples and achieve superior diagnostic results under extremely limited training data, in contrast to some mainstream data augmentation approaches.