Abstract:The structure of wind turbine gearbox is complex. When there are many faults in gear and bearing, fault diagnosis accuracy is low and some faults’ diagnosis is missed due to different fault intensities, mutual coupling between faults and noise interference. A multi-fault diagnosis method for wind turbine gearbox based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and enhanced cepstrum was proposed. Firstly, fault characteristic frequencies of different positions of gear and bearing were used to set reasonable deconvolution period, and the original signal was preprocessed by using MOMEDA. Then, enhanced cepstrum was used to further suppress noise interference and enhance fault features. Finally, prominent components in enhanced cepstrum were compared with fault characteristic frequencies of gearbox to determine the fault type. The analysis results of multi-fault vibration test data of actual wind turbine gearbox showed that the proposed method can effectively extract multi-fault feature information of gearbox.
胡爱军,严家祥,白泽瑞. 基于MOMEDA和增强倒频谱的风电机组齿轮箱多故障诊断方法[J]. 振动与冲击, 2021, 40(7): 268-273.
HU Aijun, YAN Jiaxiang, BAI Zerui. Multi-fault diagnosis method for wind turbine gearbox based on MOMEDA and enhanced cepstrum. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(7): 268-273.
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