Clustering-guided Prototypical Contrastive Learning for Bearing Fault Diagnosis Under Variable Working Conditions

Abstract Methods based on deep learning for intelligent fault diagnosis have shown good results in general diagnostic tasks. Nevertheless, these methods largely depend on the sufficient labeled data, limiting their application in the actual scenarios where the availability of labeled data is limited. Moreover, the distribution of testing data is inconsistent with that of training data because bearings operate in various working conditions, leading to the performance degradation of these approaches. To tackle these two entangled problems, we propose a novel unsupervised domain adaptation network, which presents clustering-guided prototypical contrastive learning for cross-domain fault diagnosis. More specifically, k-means clustering is first used to aggregate similar source samples and target samples separately, acquiring the centroid of each cluster and the cluster index of each sample. Then, we propose in-domain and cross-domain contrastive learning strategies based on clustering results to achieve class alignment and domain alignment across source domain and target domain. By applying in-domain contrastive learning, we make the intra-class distance smaller while making the inter-class distance larger within each domain, effectively reducing the number of samples on the class boundaries. By applying cross-domain contrastive learning, class-to-class semantic similarity across two different domains is considered, which not only retains class discriminability in each domain but aligns these two domains at both the class level and the domain level. Detailed experiments on three bearing datasets reveal that our method outperforms in fault diagnosis across diverse working conditions, achieving average accuracy improvements of 2.10%, 7.44%, and 1.17% on the JNU, HUST, and Ottawa datasets, respectively.