A Novel Clustering-Guided Framework for Rotating Machinery Fault Diagnosis With Extremely Limited Labeled Data

The scarcity of labeled data poses a significant challenge in the field of data-driven rotating machinery fault diagnosis. In practical applications, although there is a large amount of available data, only a very small portion is labeled. Unfortunately, few studies have investigated leveraging such extremely limited labeled data to improve diagnostic performance. To address this issue, a clustering-guided framework is proposed to embed the extremely limited label information into deep clustering for diagnosis performance improvement. Specifically, a clustering-guided feature learning module is proposed to unify feature learning and clustering, enabling the learned features to have optimal clustering characteristics. Meanwhile, the prior knowledge given by a few labeled data is integrated into clustering centroid initialization and cluster assignment to further improve the clustering accuracy and robustness. By introducing a virtual adversarial training regularization, the generalization of the method is enhanced. Experiment results on four datasets show that the proposed method outperforms the existing methods, which demonstrates the effectiveness of the proposed clustering-guided framework in dealing with the extreme data scarcity problem in fault diagnosis for rotating machinery.