This articleproposes an intelligent algorithm for wind turbine faults identification based on the image model of the dynamic process and the deep convolutional neural network. First, feature engineering is designed to generate the image model of a dynamic process. We performed variable refinement and data normalization preprocessing on the dataset. Then the time series data are reconstructed in the Gram angle field to form an image model. Next, an identification algorithm adapted to the image model, called multistream self-fusion net (MuSnet), is proposed. Inside the MuSnet, we use the multistream self-fusion module to replace the original convolution operation in the low-level convolution. This allows the features of heterogeneous information in the image model to be better extracted. Multiple evaluation metrics in the experiment show that the proposed method has advantages of high recognition accuracy, ability to train with fewer samples, speeding up the convergence of learning, and high robustness for wind turbine fault identification.

中文翻译：

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使用特征工程和 MuSnet 的基于图像模型的风力涡轮机故障识别

本文提出了一种基于动态过程图像模型和深度卷积神经网络的风机故障智能识别算法。首先，特征工程旨在生成动态过程的图像模型。我们对数据集进行了变量细化和数据标准化预处理。然后在Gram角场中重构时间序列数据，形成图像模型。接下来，提出了一种适应图像模型的识别算法，称为多流自融合网络（MuSnet）。在 MuSnet 内部，我们使用多流自融合模块来代替低层卷积中的原始卷积操作。这样可以更好地提取图像模型中异构信息的特征。