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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results.

Details

Title
An Improved Denoising Method for Fault Vibration Signals of Wind Turbine Gearbox Bearings
Author
Zhang, Chaohai 1 ; Zhang, Xu 2 ; Xu, Zufeng 3 ; Dai, Wei 3 ; Lu, Jie 2 

 Department of State Key Laboratory of Smart Grid Protection and Control, Nari Group Corporation, Nanjing 211106, China; [email protected] (C.Z.); [email protected] (Z.X.); [email protected] (W.D.); Department of Electrical Engineering, School of Automation, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjundadao Road, Nanjing 211106, China 
 Department of Electrical Engineering, School of Automation, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjundadao Road, Nanjing 211106, China 
 Department of State Key Laboratory of Smart Grid Protection and Control, Nari Group Corporation, Nanjing 211106, China; [email protected] (C.Z.); [email protected] (Z.X.); [email protected] (W.D.) 
First page
1004
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20751702
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2893081986
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.