Cross-view multi-stage contrastive learning with label-guided decision boundary calibration for semi-supervised bearing fault diagnosis

In practical bearing fault diagnosis scenarios, fault label annotation usually relies on costly expert knowledge and manual analysis, resulting in a very limited number of labeled samples available for model training, while abundant unlabeled vibration signals remain underutilized. To address this issue, this paper proposes a cross-view multi-stage contrastive learning method with label-guided decision boundary calibration. First, time-frequency dual-view sample pairs are constructed, and multi-stage contrastive constraints are imposed at both the encoder output layer and the projection space to mine cross-view stable structures from unlabeled samples. Meanwhile, a small number of labeled samples are introduced as class anchors to participate in the discriminative mapping from feature representations to the category space, thereby performing label-guided decision boundary calibration for the latent class regions formed by the structural aggregation of unlabeled samples. This enables the mapping to establish decision boundaries with explicit class semantics and discriminative margins in the feature space. Subsequently, the time-domain view representation of each unlabeled sample obtains a soft class assignment through the calibrated discriminative mapping, which is then used as a soft supervisory signal to constrain the frequency-domain view in a cross-view manner to produce consistent discriminative outputs, thereby driving unlabeled features from structural aggregation toward class-consistent aggregation. Experimental results show that, using only 5% labeled samples, the proposed method achieves accuracies of 96.36 and 96.88% on the self-built dataset and the PU dataset, respectively, verifying its effectiveness.