Domain Invariant Feature Learning Based on Cluster Contrastive Learning for Intelligence Fault Diagnosis With Limited Labeled Data

In real industrial scenarios, the limited available labeled fault data and existed significant data distribution differences between the source domain and the target domain emplace challenges and obstacles for cross-domain fault diagnosis. In this paper, we proposed a novel cluster contrastive learning method (CLCO) for few-shot learning (even one-shot learning) and cross-domain fault diagnosis under complex fault modes, fault severities, and variable working conditions. The proposed CLCO combines clustering strategy and contrastive learning to learn discriminate domain invariant feature representation. Moreover, the consistency in the characteristic distribution of data is viewed as the pseudo label information captured by k-means clustering, which are subsequently embedded into strengthen contrastive loss (SCL) function of proposed CLCO model. Then, the pseudo label information is exploited to guide self-supervised contrastive pre-training for learning domain invariant features based on feature similarity comparison of positive and negative pairs. Extensive experimental results demonstrate that our proposed CLCO outperforms existing state-of-the-art methods by a large margin.