A Novel Class-Wise Dynamic Anchor Weighting Fusion Framework for Cross-Condition Fault Diagnosis of Offshore Wind Turbine Drivetrain Systems

Offshore wind turbines operate under harsh and time-varying marine environments, leading to significant distribution shifts between source and target domains. These challenges are further intensified by limited fault samples, imbalanced class distributions, and difficult maintenance access in maritime scenarios. To address these issues, this paper proposes a multi-source domain adaptation method, termed the class-wise dynamic anchor weighting fusion framework (CDAWF), for offshore wind turbine drivetrain fault diagnosis. CDAWF dynamically assigns weights to different source domains according to their feature-level similarity to the target domain, enabling adaptive feature fusion across operating conditions. Convolutional neural networks are used to extract domain-specific representations, while adversarial training is introduced to reduce cross-domain discrepancies. The proposed method is validated on a multi-condition bearing fault dataset generated from high-fidelity simulations of a 5 MW offshore wind turbine drivetrain. Experimental results show that CDAWF effectively improves diagnostic robustness under domain shift, limited labeled data, and class imbalance. Compared with benchmark methods, CDAWF achieves superior classification performance and serves as a useful reference for cross-condition fault diagnosis of offshore wind turbine drivetrain systems.