小样本无监督跨域故障诊断方法OA

To address the issues of data annotation scarcity under small-sample conditions and domain shift in cross-domain applications in fault diagnosis,a domain adaptation strategy integrating contrastive learning was proposed.First,a hierarchical feature alignment mechanism was adopted to jointly optimize domain-level adversarial adaptation and class-level contrastive learning.This mechanism balanced the transfer of domain-invariant features and the preservation of class discriminability,thereby improving the fault classification accuracy in the target domain.Second,a one-dimensional domain-shared convolutional neural network(CNN)was constructed to extract the common features of data from the source domain and target domain.By combining the unsupervised domain discriminative loss with a gradient reversal layer to optimize domain-level adaptation,the transferability of domain-invariant characteristics was enhanced.Fi-nally,an unsupervised contrastive loss function was designed to optimize class-specific information.This function constrains the intra-class compactness and inter-class separability in the feature space,enabling class-level alignment under limited samples and effectively suppressing misidentification of class boundar-ies.The cross-condition unsupervised experimental results on both the constructed planetary gearbox test rig dataset and the public bearing dataset show that the diagnostic accuracy generally exceeds 95%in mul-tiple cross-condition transfer tasks.Compared with several unsupervised domain adaptation methods,the proposed method achieves a significant performance improvement,validating its effectiveness and superior-ity in small-sample unsupervised cross-condition fault diagnosis.