|Table of Contents|

 Noise suppression and characteristic frequency extraction of wind turbine vibration based on EMD correlation denoising


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[ISSN:1007-449X/CN:23-1 408/TM]

Issue:
2016年01
Page:
73-80
Research Field:
Publishing date:

Info

Title:
 Noise suppression and characteristic frequency extraction of wind turbine vibration based on EMD correlation denoising


Author(s):
 LI Hui1 LI Yang1 YANG Dong2 HU Yao-gang1 LAN Yong-sen3 LIANG Yuan-yuan4

 ( 1. State Key Laboratory of Equipment and System Safety of Power Transmission and Distribution & New Technology, Chongqing University,Chongqing 400044,China; 2. School of Electrical Engineering,Wuhan University,Wuhan 430072, China; 3. CSIC ( Chongqing) Haizhuang Wind Power Equipment Co.,Ltd. Chongqing 401122,China; 4. Chongqing KK-QIANWEI Wind Power Equipment Co.,Ltd. Chongqing 401121,China)
Keywords:
wind turbine condition monitoring noise suppression empirical mode decomposition wave-let package transform
PACS:
TM 315; TM 307
DOI:
10. 15938 /j. emc. 2016. 01. 011
Abstract:
Aiming at the issues of difficultly extracting early weak fault feature for the wind turbine vibration signals influenced by white noise and short-term disturbance noise,a noise suppression and characteristic frequency extraction method combining empirical mode decomposition ( EMD) ,correlation analysis with wavelet package transform ( WPT) were studied. This method,firstly,decomposes the vibration signals into a series of intrinsic mode functions ( IMFs) which represent different frequencies by using EMD. Then,a fault characteristic signal was restructured by accumulating the selected IMFs which characterize the fault characteristic frequencies. Secondly,the characteristic signals were analyzed by using the meth-od of autocorrelation analysis to eliminate the interference of the noises. Finally,the characteristic fre-quency is extracted by using the WPT from de-noising restructured vibration signals. WPT,EMD correla-tion denoising-WPT and wavelet hard thresholding-WPT were used to analyze the actual and simulating wind turbines bearing fault vibration signals to verify the effectiveness of the proposed method. The results of comparing with the different characteristic frequency extraction methods show that the presented charac-teristic frequency extraction method based on EMD correlation denoising-WPT can effectively depress the white noise and short-term disturbance noise,and extract early weak fault feature.

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Last Update: 2016-03-21