A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks

doi:10.16385/j.cnki.issn.1004-4523.2024.02.018

IR > 机电工程学院

	A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks
其他题名	基于改进残差网络的风电轴承故障迁移诊断方法
	Deng, Lin-Feng; Wang, Qi; Zheng, Yu-Qiao
	2024-02
发表期刊	Zhendong Gongcheng Xuebao/Journal of Vibration Engineering
ISSN	1004-4523
卷号	37 期号:2 页码:356-364
摘要	To address the low accuracy in diagnosing faults in wind turbine bearings caused by the different characteristic distribution of the source domain data and the target domain data，a fault transfer diagnosis method using improved residual neural networks is proposed. The convolution kernel and pooling kernel are set to a size suitable for the convolution operation of one-dimensional signals，allowing for direct extraction of fault features from the bearing vibration signals；Both batch normalization and case normalization are used in the one-dimensional residual network to further enhance the feature extraction ability of the model；In the model training stage，a new loss function is constructed based on the multiple kernel maximum mean discrepancy between the source domain data and the target domain data to improve the transfer learning and classification ability of the model. The effectiveness of the method is verified by conducting the experimental data of the faulty bearings. The results show that the proposed method can effectively extract the important features of bearing faults and achieve the transfer diagnosis and accurate classification of the bearing faults. This holds true even under varying speed operation conditions and when the bearing fault vibration signals are disturbed by some noise components. Therefore，this work provides a useful strategy in developing intelligent fault diagnosis technology of rotating machinery under complex working conditions. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.
关键词	Classification (of information) Computer aided diagnosis Convolution Convolutional neural networks Extraction Failure analysis Fault detection Bearing fault Convolutional neural network Diagnosis methods Fault transfer Faults diagnosis Neural-networks Residual neural network Target domain Vibration signal Wind turbine bearing
DOI	10.16385/j.cnki.issn.1004-4523.2024.02.018
收录类别	EI
语种	中文
出版者	Nanjing University of Aeronautics an Astronautics
EI入藏号	20241115750863
EI主题词	Wind turbines
EI分类号	461.1 Biomedical Engineering ; 615.8 Wind Power (Before 1993, use code 611 ) ; 716.1 Information Theory and Signal Processing ; 723.5 Computer Applications ; 802.3 Chemical Operations ; 903.1 Information Sources and Analysis
原始文献类型	Journal article (JA)
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170283
专题	机电工程学院电气工程与信息工程学院
作者单位	School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Deng, Lin-Feng,Wang, Qi,Zheng, Yu-Qiao. A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks[J]. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering,2024,37(2):356-364.
APA	Deng, Lin-Feng,Wang, Qi,&Zheng, Yu-Qiao.(2024).A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks.Zhendong Gongcheng Xuebao/Journal of Vibration Engineering,37(2),356-364.
MLA	Deng, Lin-Feng,et al."A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks".Zhendong Gongcheng Xuebao/Journal of Vibration Engineering 37.2(2024):356-364.