基于变分模态分解和谱峭度的风电机组轴承故障诊断方法

Fault Diagnosis Method for Wind Turbine Bearing Based on Variational Mode Decomposition and Spectrum Kurtosis

针对风电机组轴承故障特征提取困难的问题,将谱峭度(Spectrum Kurtosis,SK)和变分模态分解(Variational Mode Decomposition,VMD)相结合,提出一种基于VMD-SK的故障诊断新方法。首先,对采集的轴承振动信号进行VMD分解,得到若干个固有模态函数(Intrinsic Mode Function,IMF);其次,对每一个IMF分量进行傅里叶变换,并计算其平方包络;再次,利用SK的滤波特性,选取故障特征频带所在的IMF分量来构建最优包络谱;最后,通过对包络谱分析可以诊断出风力发电机轴承故障。实验结果表明,VDM-SK法可以成功地提取风电机组轴承故障的特征频率,有效区分风电机组轴承的故障类型。

Aiming at the problem that fault features of wind turbine bearing is difficult to be extracted,a new method for fault diagnosis based on VMD-SK is proposed by combining spectrum kurtosis(SK)and variational mode decomposition(VMD)in this paper.Firstly,vibration signal collected from wind turbine is decomposed into several intrinsic mode functions(IMFs)by VMD.Secondly,Fourier transform is applied to each IMF and the absolute values of spectral signals are calculated.Thirdly,using the filter characteristics of spectral kurtosis(SK),the IMF component of fault characteristic band is selected to construct the optimal envelope spectrum.Finally,the defect of wind turbine bearing can be diagnosed by analyzing the envelope spectrum.The experimental results show that the VMD-SK method can successfully extract the fault characteristic frequency and effectively distinguish the fault type of wind turbine bearing.