FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON IMPROVED EMPIRICAL WAVELET TRANSFORM AND FRACTAL FEATURE SET

被引：0

作者：

Sun K. ^{[1
]}

Jin J. ^{[1
]}

Li C. ^{[1
,2
]}

Ye K. ^{[1
]}

Xu Z. ^{[1
]}

机构：

[1] University of Shanghai for Science and Technology, Energy and Power Engineering Institute, Shanghai

[2] Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 05期

关键词：

empirical wavelet transform; fault detection; fractal Gaussian noise grey wolf optimizer; gearbox; improved continuous average spectral negentropy; support vector machines; wind turbines;

D O I：

10.19912/j.0254-0096.tynxb.2021-0980

中图分类号：

学科分类号：

摘要：

Since the vibration response signal of wind turbine gearbox is highly nonlinear and non-stationary，under the premise of considering the adaptive adjustment of the average amplitude to the average spectral negative entropy of time and frequency domain component weight，the improved continuous average spectral negentropy（ICASN）method is proposed to reflect the detail complexity characteristics of signals. Moreover，ICASN is introduced into Empirical Wavelet Transform（EWT）to replace Fourier spectrum as the basis of frequency band division. According to ICASN- EWT decomposition of vibration signals，the feature components are screened based on Improved Average Spectral Negentropy （IASN） to eliminate signal redundancy and noise influence. Then，the fractal characteristics of each sensitive component are analyzed and the high dimensional feature set is constructed. Meanwhile，Manifold Learning（ML）is used for dimension reduction. Moreover，take fractal Gaussian Noise Grey Wolf Optimizer（FGNGWO）to optimize the key parameters of Support Vector Machine（SVM）. The vector set after dimensionality reduction is input into the optimized support vector machine for fault identification and diagnosis，and the accuracy is up to 100%. © 2023 Science Press. All rights reserved.

引用

页码：310 / 319

页数：9

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