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Weak fault diagnosis of wind turbine main bearing based on SVDP Gini index image and ARLD

Published:2023-08-14 author:HUANG Xiang-sheng, SUN Qiu-ju, TANG Xiao-mao, et al. Browse: 392 Check PDF documents

Weak fault diagnosis of wind turbine main bearing based on SVDP

Gini index image and ARLD

HUANG Xiang-sheng1, SUN Qiu-ju1, TANG Xiao-mao2, ZHONG Ming-qiu3

(1.Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200000, China; 2.China Three Gorges Corporation

Fujian Energy Investment Co., Ltd., Fuzhou 350000,China; 3.Fujian Xinneng Offshore Wind Power R & D

Center Co., Ltd., Fuzhou 350000, China)

Abstract: In the strong background noise environment, the vibration impact caused by the local fault of the main bearing during the service of the wind turbine is easily disturbed. At the same time, due to the influence of the complex and variable vibration transmission path, it is often difficult to accurately identify the fault of the main bearing. To solve this problem, a main bearing fault diagnosis method based on the singular value decomposition packet (SVDP) Gini index diagram and the adaptive RichardsonLucy deconvolution (ARLD) was proposed, to extract weak fault features in strong background noise environment. Firstly, the decomposition level of SVDP was set to process the original signal, and the Gini indexes of different level components were calculated to construct SVDP Gini index image. Then, the optimal component could be separated from original signal and the signal noise ratio could be improved. Further, the optimal morphological control parameter of deconvolution algorithm was automatically acquired by whale optimization algorithm, the deconvolution process was performed on the optimal component to restrain the noise interferences and enhance the impact features. Finally, the envelope demodulation analysis of deconvolution signal was carried out using Teager energy operator, and the fault location of main bearing could be judged according to the feature frequency spectral lines in the envelope spectrum. The effectiveness and robustness of the diagnosis method based on SVDP Gini index image and ARLD were respectively verified by simulation signals and wind power field measured data. The research results show that the fault diagnosis method based on SVDP Gini index diagram and ARLD can effectively extract the fault characteristic frequency and multiple frequency components of the main bearing of wind turbine, and then accurately judge the weak fault of the main bearing. This method can provide some reference for weak fault diagnosis of main bearing of wind turbine in practical engineering.

Key words: background noise; rolling bearing fault diagnosis; singular value decomposition packet(SVDP) Gini index image method; whale optimization algorithm(WOA); adaptive RichardsonLucy deconvolution (ARLD); engineering measured signal; robustness

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