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利用具体的非平稳齿轮箱振动信号,分别应用局域均值分解方法(Local Mean Decomposition,LMD)和经验模态分解方法(Empirical Mode Decomposition,EMD)进行了模态分解,并计算得出能量熵。物理意义明确且非常直观,用LMD方法分解齿轮箱振动信号模态混叠程度要轻于EMD方法分解所得模态混叠程度。同时,从端点效应和分解速度两方面将两种分解方法做了对比,LMD方法抑制端点效应的能力强于EMD方法,且分解速度较EMD方法快。
Using the vibration signal of the non-stationary gearbox, the modal decomposition was performed by using Local Mean Decomposition (LMD) and Empirical Mode Decomposition (EMD) respectively, and the energy entropy was calculated. The physical meaning is clear and very intuitive. Using the LMD method to decompose the modal aliasing of the gearbox vibration signal is less than that of the EMD method. At the same time, the two decomposition methods are compared in terms of the endpoint effect and the decomposition speed. The ability of the LMD method to suppress the endpoint effect is stronger than that of the EMD method, and the decomposition speed is faster than that of the EMD method.