Multi-Class Gearbox Fault Diagnosis via Pre-Trained Model-based Domain Adaptation with Healthy-Only Target Data

Accurate gearbox fault diagnosis under varying operational speeds is critical for industrial predictive maintenance. A significant challenge is domain shift, where models trained under one condition fail to generalize to another, especially when only healthy data from the target domain is available for training. This study proposes a novel domain adaptation framework, CDANet, that directly leverages raw sensor data to perform multi-class fault classification without manual feature engineering. The model combines a lightweight CNNbased temporal feature extractor with a frozen DistilBERT encoder to capture transferable, domain-invariant representations, combined with a maximum mean discrepancy loss to align the feature distributions between the source and target domains using only healthy samples. Experimental results demonstrate that our proposed model significantly outperforms conventional deep learning approaches, achieving high classification accuracy across six domain adaptation tasks. This work validates the effectiveness of applying pre-trained models in domain adaptation for gearbox fault diagnosis under real-world domain shift constraints.