Wind turbine drivetrain fault diagnosis and intelligent O&M: a review

Abstract

As wind turbines evolve toward larger capacities, fleet-level clustering, and operation under complex conditions, fault mechanisms in key drivetrain components show multi-physics coupling and complex evolution, creating a major bottleneck in condition monitoring: models are often constructible but hard to generalize. Although deep learning is effective for end-to-end feature extraction, industrial data challenges - such as limited samples, long-tailed distributions, and domain shifts - severely restrict model generalization and engineering applicability. To address these issues, this paper systematically reviews fault diagnosis and intelligent operation and maintenance (O&M) technologies for wind turbine drivetrains. It analyzes generative-model-based sample augmentation methods and their limitations in authenticity and generalization improvement; examines the roles of semi-supervised, contrastive, and cost-sensitive learning in handling imbalanced and unlabeled data; and discusses how multimodal fusion, federated learning, and domain adaptation help alleviate data silos and environmental drift. Finally, it summarizes major challenges, including cross-domain generalization, explainability, and edge deployment, and proposes a future adaptive O&M paradigm integrating physical mechanisms with data-driven methods across the full asset lifecycle.