Abstract This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain.

中文翻译：

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风力涡轮机状态监测的机器学习方法：综述

摘要 本文回顾了机器学习 (ML) 模型的最新文献，这些模型已用于风力涡轮机的状态监测（例如叶片故障检测或发电机温度监测）。我们通过典型的 ML 步骤对这些模型进行分类，包括数据源、特征选择和提取、模型选择（分类、回归）、验证和决策。我们的研究结果表明，大多数模型使用 SCADA 或模拟数据，几乎三分之二的方法使用分类，其余的依靠回归。神经网络、支持向量机和决策树是最常用的。我们最后讨论了该领域未来工作的主要领域。