Condition monitoring and fault diagnosis of main bearings of large-scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi-layer perceptron (SSAE-MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine-tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.

中文翻译：

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SSAE-MLP：用于大型风力涡轮机主轴承温度预测的基于堆叠稀疏自编码器的多层感知器

大型风力发电机组主轴承的状态监测和故障诊断对于提高其可靠性和降低运行维护成本至关重要，尤其是在早期阶段。为了实现这一目标，本文提出了一种新的深度学习方法，称为堆叠稀疏自动编码器多层感知器（SSAE-MLP），该方法具有新的框架，利用监控和数据采集（SCADA）数据进行风力涡轮机主轴承温度预测。提取与主轴承温度变化相关的SCADA参数变量后，构建输入特征向量。然后，将多个稀疏自编码器堆叠起来，通过应用贪婪的逐层无监督学习算法来学习输入数据中的深层特征。最后，回归预测器被添加到堆叠稀疏自动编码器模型的顶层，用于监督学习以微调整个网络。对比实验表明，所提出的方法在风力涡轮机主轴承温度预测方面具有优越的性能。