Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing environmental pollution, and enhancing the sustainability of energy supply. Health monitoring of wind turbines is a crucial technology to ensure the quality of grid-connected power. Insufficient labeled data and class imbalance problems are two critical issues for intelligent fault diagnosis of wind turbines. In this article, an intelligent fault diagnosis method based on stacked capsule autoencoders is proposed to address the issues of inadequate labeled data and class imbalance. A prior knowledge-based convolution layer is applied to optimize the initialization of capsules, making it more conducive to learning spectral information. The pose representations of parts and objects can be improved, and a method for embedding spectral templates is proposed. The stacked capsule autoencoder in this study can learn partial templates unsupervised through likelihood estimation and establish the mapping between capsules and fault types. The experimental results, obtained from the CWRU dataset and a private dataset from a wind turbine drive-train simulation platform, demonstrate that the proposed method is robust to imbalanced and small-sized datasets. It can perform stable and effective unsupervised training by utilizing a sufficient amount of normal class data to expedite learning convergence.

中文翻译：

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基于堆叠胶囊自编码器的类不平衡和小数据量风电机组故障诊断


风电对于减少二氧化碳排放、减少环境污染、增强能源供应的可持续性具有重要的战略意义。风力发电机组的健康监测是保证并网电力质量的关键技术。标记数据不足和类别不平衡问题是风力发电机智能故障诊断的两个关键问题。本文提出了一种基于堆叠胶囊自动编码器的智能故障诊断方法，以解决标记数据不足和类别不平衡的问题。应用基于先验知识的卷积层来优化胶囊的初始化，使其更有利于学习光谱信息。可以改进零件和物体的姿态表示，并提出了一种嵌入光谱模​​板的方法。本研究中的堆叠胶囊自动编码器可以通过似然估计无监督地学习部分模板，并建立胶囊和故障类型之间的映射。从 CWRU 数据集和风力涡轮机传动系统仿真平台的私有数据集获得的实验结果表明，所提出的方法对于不平衡和小规模数据集具有鲁棒性。利用足够量的正常类数据，可以进行稳定有效的无监督训练，加快学习收敛。