WIND TURBINE FAULT DIAGNOSIS METHOD BASED ON PARALLEL CONVOLUTIONAL NEURAL NETWORK

被引：0

作者：

Meng L. ^{[1
]}

Su Y. ^{[1
]}

Xu T. ^{[1
]}

Kong X. ^{[1
]}

Lan X. ^{[1
]}

Li Y. ^{[1
]}

机构：

[1] Mechanical Engineering School, Shandong University of Technology, Zibo

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 05期

关键词：

bearing; convolutional neural network; fault diagnosis; feature enhancement; feature visualzation; wind turbines;

D O I：

10.19912/j.0254-0096.tynxb.2022-0052

中图分类号：

学科分类号：

摘要：

In view of the problems of difficulty in bearing weak fault signal feature extraction and poor performance of fault diagnosis model for wind turbine，a fault diagnosis method based on parallel convolutional neural network is proposed. Firstly，1-Dimensional signals are converted into 2- dimensional time- frequency feature maps using continuous wavelet transform. Secondly，a parallel convolutional neural network structure is constructed，which consists of large convolutional layer and parallel convolutional layer. The large convolutional layer can quickly extract all the features of the input layer. The parallel convolutional layer is a two-layer small convolution parallel structure，which can effectively identify fault information. Then，the feature fusion layer is adopted to achieve feature enhancement inside the diagnosis model and reduce the complexity of the model，which combines the fault features extracted by two parallel convolutional layers. Finally，experimental verification showes that the fault diagnosis accuracy of the proposed model for bearing is 98.25%. © 2023 Science Press. All rights reserved.

引用

页码：449 / 456

页数：7

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