【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。更多还原

【Abstract】 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 time-varying multi-component signal.Also the GVMD method is compared with the continuous wavelet transform method and Hilbert-Huang 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.更多还原