Diagnosis of Wind Turbine Planetary Gearbox Faults Based on Adaptive EEMD and Hierarchical Fractal Dimension
Li Dongdong1, 2, hou Wenlei1, Zheng Xiaoxia3, Wang Hao1
1. Electric Power Engineering Shanghai University of Electric Power Shanghai 200090 China. 2. Shanghai Higher Institution Engineering Research Center of High Efficiency Electricity Application Shanghai 200090 China. 3. Automation Engineering Shanghai University of Electric Power Shanghai 200090 China
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.
李东东, 周文磊, 郑小霞, 王浩. 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[J]. 电工技术学报, 2017, 32(22): 233-241.
Li Dongdong, hou Wenlei, Zheng Xiaoxia, Wang Hao. Diagnosis of Wind Turbine Planetary Gearbox Faults Based on Adaptive EEMD and Hierarchical Fractal Dimension. Transactions of China Electrotechnical Society, 2017, 32(22): 233-241.
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2017, Vol. 32 
