FAULT DIAGNOSIS METHOD OF WIND TURBINE PLANETARY GEARBOX BASED ON ENHANCED CONVOLUTIONAL NEURAL NETWORK

被引：0

作者：

Liang S. ^{[1
]}

Gu Y. ^{[1
,2
]}

Luo Y. ^{[1
]}

Chen C. ^{[1
,2
]}

机构：

[1] School of Mechanical Engineering, Shenyang University of Technology, Shenyang

[2] Liaoning Vibration and Noise Control Professional Innovation Center, Shenyang

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 02期

关键词：

dilated convolutional neural network; fault diagnosis; inception net; planetary gearbox; wind turbines;

D O I：

10.19912/j.0254-0096.tynxb.2021-1109

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the health maintenance and state detection of wind turbine planetary gearboxes are difficult to diagnose, a fault diagnosis research method of the initial dilated convolutional neural network(IDCNN)that combines the initial net and dilated convolution is proposed in this paper. This method first constructs an initial dilated convolutional layer to expand the receptive field to enrich the learned fault features. Subsequently, in order to facilitate signal input and ensure rich information, a preprocessing method of transforming the one- dimensional original signal sequence into a two- dimensional matrix will be adopted. Finally, the generated two-dimensional signal is input into IDCNN for model training, and the model is evaluated with test data. The experimental results show that the proposed IDCNN method has high accuracy in the fault diagnosis of the planetary gearbox of the wind turbine. In the comparison results, the diagnosis accuracy of the proposed method is higher than that of the traditional deep learning method. © 2023 Science Press. All rights reserved.

引用

页码：146 / 152

页数：6

共 17 条

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