Domain contrastive-based prototype discriminant network for few-shot rotating machinery fault diagnosis under variable working conditions

Abstract The vigorous development of data-driven methods has promoted the application of intelligent fault diagnosis technology in various manufacturing industries. However, it is difficult for the model to obtain satisfactory diagnosis results and generalization performance with small samples under variable working conditions. To solve these problems, a new prototype discriminant network based on domain contrast learning is proposed, which has self-supervised few-shot cross-domain fault diagnosis capability. First, sample pairs are constructed based on differences in data domain distribution. The domain-invariant features between classes are extracted by increasing the distance between classes and reducing the differences within classes using unsupervised training. Then, a prototype discriminant network is used to accurately diagnose under few-shot and variable working conditions. To realize accurate diagnosis in two typical rotating machinery diagnosis cases of bearings and gearboxes, the performance of the proposed framework is verified, and higher diagnostic accuracy and generalization performance are achieved compared to existing methods.