在工程实践中，风机故障诊断面临训练故障类别与实际故障类别不同的情况，为实现对风机未知故障的诊断，需要将训练过程中习得的故障特征信息迁移至未知故障中。不同于传统方法直接建立故障样本与故障类别间映射关系的做法，为完成故障特征迁移，提出一种基于零样本学习的风机故障诊断方法。通过描述每种故障的故障属性建立故障属性矩阵，将其嵌入故障样本空间与故障类别空间之中；并基于卷积神经网络建立故障属性学习器，基于欧式距离建立故障分类器，形成从故障样本预测故障属性进而分类故障的诊断流程。最后通过与其他零样本学习方法的对比验证了所提故障诊断方法的有效性和优越性。

In engineering practice, wind turbine fault diagnosis faces situations where the training fault category is different from the actual fault category. To achieve the diagnosis of unknown wind turbine faults, it is necessary to transfer the fault feature information learned during the training process to the unknown fault. Unlike traditional methods that directly establish the mapping relationship between fault samples and fault categories, a zero sample fault diagnosis method for wind turbines based on fault attributes is proposed to complete fault feature migration. The fault attribute matrix is established by describing the fault attributes of each fault, and it is embedded into the fault Sample space and fault category space; The fault attribute learner is established based on Convolutional neural network, and the fault classifier is established based on Euclidean distance, forming the diagnosis process of predicting fault attributes from fault samples and then classifying faults. Finally, the effectiveness and superiority of the proposed fault diagnosis method were verified through comparison with other zero-shot learning methods.