Fault feature extraction of a wind turbine gearbox using adaptive parameterless empirical wavelet transform

被引：0

作者：

Ding X. ^{[1
]}

Xu J. ^{[1
]}

Teng W. ^{[2
]}

Wang W. ^{[2
]}

机构：

[1] Luneng Group Co., Ltd., Beijing

[2] Key Laboratory of Power Station Energy Transfer Conversion and System, Ministry of Education, North China Electric Power University, Beijing

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 08期

关键词：

Adaptive; Empirical wavelet transform; Fault feature extraction; Margin factor; Parameterless;

D O I：

10.13465/j.cnki.jvs.2020.08.014

中图分类号：

学科分类号：

摘要：

Wind turbines operate as an equipment cluster, bringing massive vibration signals due to their complex structures and multiply vibration measures. Only analysing the vibration signals to detect fault by human is challenging. In this paper, a fault feature extraction method for wind turbine gearboxes was proposed on the basis of the parameterless empirical wavelet transform. The scale space method and empirical law were utilized to automatically split the Fourier spectrum of the vibration signal, and different frequency bands were obtained. A series of empirical wavelet filters were designed based on the split frequency bands to decompose the signal into multiply empirical modes. The metric of margin factor was adopted to sort the empirical modes, and the empirical mode with maximum margin factor was recognized as the most sensitive one to fault. The proposed method is adaptive without any presented parameters. The fault signals from an experimental platform and a real wind turbine gearbox verified the proposed method. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：99 / 105and117

共 13 条

[1] (2017)
[2] LI Heng, ZHANG Qin, QIN Xianrong, Et al., Fault diagnosis method for rolling bearings based on short-time fourier transform and convolution neural network, Journal of Vibration and Shock, 37, 19, pp. 124-131, (2018)
[3] QIN Yi, QIN Shuren, MAO Yongfang, Demodulation approach based on wavelet ridge and its application in fault diagnosis of rotating machinery, Journal of Mechanical Engineering, 45, 2, pp. 231-237, (2009)
[4] ANTONI J., Cyclic spectral analysis of rolling-element bearing signals: facts and fictions, Journal of Sound and Vibration, 304, 3, pp. 497-529, (2007)
[5] ANTONI J, RANDALL R B., The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing, 20, 2, pp. 308-331, (2006)
[6] LI G, ZHAO Q., Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection, Journal of Sound and Vibration, 390, pp. 218-231, (2017)
[7] LI Kangqiang, FENG Zhipeng, Modal parameter identification based on empirical mode decomposition and energy operator for planetary gearboxes, Journal of Vibration and Shock, 37, 8, pp. 1-8, (2018)
[8] HU A J, YAN X A, XIANG L., A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension, Renewable Energy, 83, pp. 767-778, (2015)
[9] GILLES J., Empirical wavelet transform, IEEE Transactions on Signal Processing, 61, 16, pp. 3999-4010, (2013)
[10] HUANG Nantian, ZHANG Shuxin, CAI Guowei, Et al., Mechanical fault diagnosis of high voltage circuit breakers utilizing empirical wavelet transform and one-class support vector machine, Chinese Journal of Scientific Instrument, 12, pp. 2773-2781, (2015)

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