广义变分模态分解方法及其在变工况齿轮故障诊断中的应用

摘要

变分模态分解(Variational Mode Decomposition,VMD)是近年来提出的非平稳信号分解方法,通过将信号分解问题转化为变分约束问题,从而实现多变量信号的模态分离.但VMD方法在分析时变多分量信号时存在模态混叠现象.对此,提出了一种适合分析时变模态的信号处理方法——广义变分模态分解(Generalized VMD,GVMD).通过分析仿真信号,将GVMD与小波变换,原VMD和希尔伯特黄变换等方法进行了对比,结果表明,新提出的GVMD方法分解结果更精确,时频分辨率更高.最后,将GVMD方法应用于变转速齿轮振动信号故障特征的识别,结果表明了论文方法的有效性.%The variational mode decomposition (VMD) is a recently proposed non-stationary signal analysis method.However,the mode mixing will occur when the VMD is used to analyze the time-varying multi-component signal.In this paper,a new signal decomposition method called generalized variational mode decomposition (GVMD) is proposed for analyzing the timevarying multi-component signal.Also the GVMD method is compared with the continuous wavelet transform method and HilbertHuang transform by analyzing the simulation signal.The results show that the decomposition of GVMD is more accurate and having higher time-frequency resolution.Finally,the proposed method is applied to identify the time-varying fault identification under variable working conditions from the gear vibration signals and the analysis results verified the effectiveness of the proposed method.