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The processing of rotor startup signals based on empirical mode decomposition

机译：基于经验模态分解的转子启动信号处理

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In this paper, we applied empirical mode decomposition method to analyse rotor startup signals, which are non-stationary and contain a lot of additional information other than that from its stationary running signals. The methodology developed in this paper decomposes the original startup signals into intrinsic oscillation modes or intrinsic modes function (IMFs). Then, we obtained rotating frequency components for Bode diagrams plot by corresponding IMFs, according to the characteristics of rotor system. The method can obtain precise critical speed without complex hardware support. The low-frequency components were extracted from these IMFs in vertical and horizontal directions. Utilising these components, we constructed a drift locus of rotor revolution centre, which provides some significant information to fault diagnosis of rotating machinery. Also, we proved that empirical mode decomposition method is more precise than Fourier filter for the extraction of low-frequency component.
机译：在本文中，我们采用经验模式分解方法来分析转子启动信号，该信号是非平稳的，除了其平稳运行信号之外，还包含许多其他信息。本文开发的方法将原始启动信号分解为固有振荡模式或固有模式函数（IMF）。然后，根据转子系统的特性，通过相应的IMF获得了Bode图的旋转频率分量。该方法无需复杂的硬件支持即可获得精确的临界速度。从这些IMF沿垂直和水平方向提取低频分量。利用这些组件，我们构建了转子旋转中心的漂移轨迹，这为旋转机械的故障诊断提供了一些重要信息。此外，我们证明了经验模态分解方法比傅里叶滤波器更精确地提取低频分量。

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