基于自适应EEMD和分层分形维数的风电机组行星齿轮箱故障检测

doi:10.19595/j.cnki.1000-6753.tces.160694

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Abstract The main defect of the traditional ensemble empirical mode decomposition (EEMD) is that the important parameters of the added white noise are set by artificial experience. In the paper, an adaptive EEMD is proposed based on the study of the factors that caused the modal aliasing. This method could set the parameters for different signals adaptively to achieve optimal decomposition effectiveness. Firstly, the singular value decomposition (SVD) was used to decompose and reconstruct the signals. Next, the reconstruction signals were used to determine the parameters of the white noise adaptively. Finally, using the proposed method, the signals were decomposed to a series of intrinsic mode function (IMF). Fractal dimension is good for the evaluation of the characteristics of IMF, so it is effective to identify different types of vibration signals. The hierarchical fractal dimension was used to improve the accuracy and efficiency of signal recognition. The experimental and simulation results of the gearbox of the wind turbine show that the proposed method is more effective compared with the existing techniques.

Key words： Wind turbines, diagnosis of planetary gearbox faults, adaptive ensemble empirical mode decomposition (EEMD), hierarchical fractal

Received: 14 May 2016 Published: 05 December 2017

PACS:	TM614
	TM315

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	Articles by authors
	Li Dongdong
	hou Wenlei
	Zheng Xiaoxia
	Wang Hao

Cite this article:

Li Dongdong,hou Wenlei,Zheng Xiaoxia等. Diagnosis of Wind Turbine Planetary Gearbox Faults Based on Adaptive EEMD and Hierarchical Fractal Dimension[J]. Transactions of China Electrotechnical Society, 2017, 32(22): 233-241.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160694 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I22/233

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