Intelligent fault diagnosis of wind turbine rolling bearings based on BFD and MSCNN

被引：0
|
作者：
Deng M. [1 ,2 ]
Deng A. [1 ,2 ]
Zhu J. [1 ,2 ]
Shi Y. [1 ,2 ]
Ma T. [1 ,2 ,3 ]
机构：
[1] School of Energy and Environment, Southeast University, Nanjing
[2] National Engineering Research Center of Turbo-Generator Vibration, Southeast University, Nanjing
[3] Guohua Taicang Power Plant Co. Ltd., Suzhou
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2021年 / 51卷 / 03期
关键词：
Bandwidth Fourier decomposition(BFD); Fault diagnosis; Multi-scale convolutional neural network(MSCNN); Rolling bearing; Wind turbine;
D O I：
10.3969/j.issn.1001-0505.2021.03.022
中图分类号：
学科分类号：
摘要：
A novel intelligent fault diagnosis method for wind tribune rolling bearings is proposed on the basis of bandwidth Fourier decomposition (BFD) and multi-scale convolutional neural network (MSCNN). First, the measured vibration signal is decomposed into bandwidth mode functions (BMFs) through the BFD method. Then, the Hilbert order transform (HOT) is employed to obtain the envelope order spectra (EOS) of BMFs. After that, the effective component containing the most fault information is selected according to the characteristic order ratio (COR). Finally, an MSCNN is established to learn the mapping relationship between the EOS of the effective component and the fault category.The experimental results demonstrate that taking the EOS for fault identification can improve the generalization ability of the fault diagnosis model under different working conditions. The test accuracy exceeds 97%, indicating the feasibility of the proposed method in practical applications. © 2021, Editorial Department of Journal of Southeast University. All right reserved.
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页码：521 / 528
页数：7
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