The early detection of blade icing is gaining increasing attention due to its importance in guaranteeing wind turbine safety and operation efficiency. In this study, a wind turbine icing fault detection method based on discriminative feature learning is proposed. First, a stacked autoencoder (SAE) is trained to generate representations, which utilizes a large amount of normal operating data, as well as time series correlation information. Second, discriminative features are obtained by combining the original data, SAE-extracted features, and the residual vector. Third, the sparse linear discriminant analysis is performed on the discriminative features to achieve simultaneous feature selection and dimension reduction. Finally, the wind turbine operation status is examined using the learned discriminative feature. The proposed discriminative feature learning-based fault detection scheme is tested on a benchmark wind turbine icing dataset. Results of the comparative trial verify th...

中文翻译：

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判别特征学习用于风机叶片结冰故障检测

由于叶片结冰在保证风力涡轮机安全性和运行效率方面的重要性，因此对其早期发现的关注日益受到关注。本文提出了一种基于判别特征学习的风力发电机结冰故障检测方法。首先，对堆叠式自动编码器（SAE）进行训练以生成表示形式，该表示形式使用大量的正常操作数据以及时间序列相关信息。其次，通过组合原始数据，SAE提取的特征和残差矢量来获得区分特征。第三，对判别特征进行稀疏线性判别分析，以实现特征的同时选择和降维。最后，使用学习到的判别特征检查风力涡轮机的运行状态。在基准风力涡轮机结冰数据集上测试了基于判别特征学习的故障检测方案。比较试验的结果验证了...