Energies, Vol. 19, Pages 1370: AI-Driven Fault Detection and O&M for Wind Turbine Drivetrains: A Review of SCADA, CMS and Digital Twin Integration
Energies doi: 10.3390/en19051370
Authors:
Ning Jia
Jiangzhe Feng
Zongyou Zuo
Zhiyi Liu
Tengyuan Wang
Chang Cai
Qingan Li
The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many existing studies remain fragmented and insufficiently address practical challenges such as heterogeneous data, sparse fault labels, and cross-site generalization. This review provides an engineering-oriented synthesis of AI-based methods for wind turbine fault detection and O&M, focusing on drivetrain diagnostics as a representative application. The literature is organized along an end-to-end O&M workflow, including SCADA-based condition monitoring, component-level fault diagnosis, health assessment and remaining useful life estimation, multi-modal blade inspection, and DT (Digital Twin) integration. Traditional ML (machine learning), ensemble methods, deep learning, physics-informed learning, and transfer learning are reviewed with respect to their data requirements, operational assumptions, and deployment constraints. Beyond algorithmic performance, this review discusses data governance, alarm design, model updating, and interpretability, and summarizes public datasets and emerging data resources. The aim is to bridge methodological advances and practical O&M requirements, supporting reliable and deployable AI applications in wind energy systems.