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Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-01-01 , DOI: 10.1109/tim.2020.3048799
Xiaoxia Yu , Baoping Tang , Kai Zhang

The fault diagnosis of the gearbox of wind turbines is a crucial task for wind turbine operation and maintenance. Although a convolutional neural network can extract the related information of adjacent sampling points using kernels, traditional deep learning methods have not leveraged related information from points with a large span of vibration signal data. In this article, a novel fast deep graph convolutional network is proposed to diagnose faults in the gearbox of wind turbines. First, the original vibration signals of the wind turbine gearbox are decomposed by wavelet packet, which presents time–frequency features as graphs. Then, graph convolutional networks are introduced to extract the features of points with a large span of the defined graph samples. Finally, the fast graph convolutional kernel and the particular pooling improvement are used to reduce the number of nodes and achieve fast classification. Experiments on two data sets are performed to verify the efficacy of the proposed method.

中文翻译:

使用快速深度图卷积网络的新方法进行风力涡轮机齿轮箱故障诊断

风电机组齿轮箱的故障诊断是风电机组运行维护的关键任务。虽然卷积神经网络可以使用内核提取相邻采样点的相关信息,但传统的深度学习方法并没有利用来自具有大跨度振动信号数据的点的相关信息。在本文中,提出了一种新颖的快速深度图卷积网络来诊断风力涡轮机齿轮箱中的故障。首先,将风力发电机齿轮箱的原始振动信号进行小波包分解,将时频特征呈现为图形。然后,引入图卷积网络来提取具有大跨度定义的图样本的点的特征。最后,快速图卷积核和特定池化改进用于减少节点数量并实现快速分类。对两个数据集进行实验以验证所提出方法的有效性。
更新日期:2021-01-01
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