The safe and reliable operation of the pitch system is essential for the stable and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wide variety of variables, leading to redundant features that interfere with the accuracy of final diagnosis results, making it difficult to meet requirements. Also, the problem of extracting only local features while ignoring global information is present in the feature extraction process using the deep Convolutional Neural Network (CNN) model. To address these issues, the global average correlation coefficient is proposed in this article to measure the correlation between multiple variables in SCADA data. By considering the correlation among multiple variables comprehensively, redundant features are effectively eliminated, enhancing the accuracy of fault diagnosis. Furthermore, a new local amplification fusion architecture network (LAFA-Net) based on multi-head attention (MHA) is introduced. An efficient local feature extraction module, designed to enhance the model’s perception of detailed features while maintaining global context information, is first introduced. LAFA-Net integrates the advantages of CNN and MHA, efficiently extracting and fusing valuable features from filtered data for both local and global aspects. Experiments on real pitch fault data demonstrate that the global average correlation coefficient effectively screens out redundant features in the dataset that negatively impact fault diagnosis results, thereby improving diagnosis efficiency and accuracy. The LAFA-Net model, capable of accurately diagnosing multiple types of pitch faults, shows a superior classification effect and accuracy compared to several advanced models, along with a faster convergence speed.

变桨系统的安全可靠运行对于风力发电机组（WT）的稳定高效运行至关重要。监控与数据采集系统（SCADA）采集的变桨故障数据往往包含多种变量，导致特征冗余，影响最终诊断结果的准确性，难以满足要求。此外，深度卷积神经网络（CNN）模型的特征提取过程中还存在仅提取局部特征而忽略全局信息的问题。为了解决这些问题，本文提出全局平均相关系数来衡量SCADA数据中多个变量之间的相关性。通过综合考虑多个变量之间的相关性，有效消除冗余特征，提高故障诊断的准确性。此外，引入了一种基于多头注意力（MHA）的新的局部放大融合架构网络（LAFA-Net）。首先引入了一种高效的局部特征提取模块，旨在增强模型对细节特征的感知，同时保持全局上下文信息。 LAFA-Net融合了CNN和MHA的优点，从局部和全局方面的过滤数据中有效地提取和融合有价值的特征。对真实桨距故障数据的实验表明，全局平均相关系数有效地筛选出了数据集中对故障诊断结果产生负面影响的冗余特征，从而提高了诊断效率和准确性。 LAFA-Net模型能够准确诊断多种类型的俯仰故障，与几种先进模型相比，表现出优越的分类效果和准确性，并且收敛速度更快。