Maximum Margin Local Domain Adaptation for Bearing Fault Diagnosis Under Multiple Operating Conditions

Unsupervised domain adaptation (UDA) has been extensively studied for bearing fault diagnosis under multiple operating conditions by mitigating distribution discrepancies across domains. However, in cross-domain imbalanced scenarios, bearing vibration signals are affected by both feature shift and class imbalance. Although a robust decision boundary learned from the source domain is critical for reliable transfer, classifier discriminability and robustness can be degraded by hard samples located near the boundary. As a result, the decision boundary may become ambiguous during adaptation, leading to degraded diagnostic performance in the target domain. To address these issues, a Maximum Margin Local Domain Adaptation (MMLDA) framework is proposed in which a multi-scale convolutional neural network is adopted as the backbone. Three core components are integrated into our framework: first, category-level reweighting to alleviate source-domain class imbalance; second, cross-domain local category alignment to reduce fine-grained feature discrepancies and feature shift; and finally, maximum-margin loss regularization to impose adaptive margin constraints on hard samples for improved decision boundary robustness. To evaluate the proposed method, cross-domain imbalanced transfer tasks under multiple operating conditions were constructed on two public bearing fault datasets, and comparative experiments were conducted. The results under different imbalance protocols demonstrate improved robustness and generalization of MMLDA.