Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis

Abstract

Domain generalization methods can effectively identify machinery faults under unseen new target working conditions. Nevertheless, most of them rely on data from multiple source domains that are available for model training. However, it is laborious difficult to collect complete monitoring data of machinery under multiple working conditions. Confronting the scenario that only one working condition is available, this paper proposes a novel single domain generalization model, termed multi-scale style generative and adversarial contrastive networks (MSG-ACN), which learns diagnosis knowledge from the single working condition and generalizes it to new working conditions. The main idea of the MSG-ACN model is to generate diverse samples in an extended domain via a domain generation module, and extract domain-invariant features from the source and extended domains via a diagnosis task module. A multi-scale style generation strategy is established to ensure that the generated samples contain abundant state information with the aids of multi-scale convolutional kernels and style learning. Furthermore, an adversarial contrastive learning strategy is designed to promote the learning of class-wise domain-invariant representations while maintaining the diversity of the generated samples. Extensive generalization diagnosis experiments on two datasets verify the superiority of the proposed method over the state-of-the-art fault diagnosis methods.