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

Sun Kang; Jin Jiangtao; Li Chun; Ye Kehua; Xu Zifei

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

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (5) : 310-319. DOI: 10.19912/j.0254-0096.tynxb.2021-0980

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

Sun Kang¹, Jin Jiangtao¹, Li Chun^1,2, Ye Kehua¹, Xu Zifei¹

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Abstract

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%.

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

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Sun Kang, Jin Jiangtao, Li Chun, Ye Kehua, Xu Zifei. FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON IMPROVED EMPIRICAL WAVELET TRANSFORM AND FRACTAL FEATURE SET[J]. Acta Energiae Solaris Sinica. 2023, 44(5): 310-319 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0980

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