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Instantaneous Amplitude-Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

机译:基于BEMD和HILBERT变换的转子故障瞬时幅度频率特征提取

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The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.
机译:从旋转机器不同方向传播的振动信号可以包含各种特征信息。提出了一种基于双变量经验模式分解(BEMD)的新型特征提取方法,用于全面提取故障特征。在这项工作中,通过模拟确定信号投影方向的数量,并且使用BEMD增加基于能量阈值的能量结束条件来增强分解质量。沿两个正交方向产生混合振动信号。然后,使用BEMD方法,所获取的振动信号可以以旋转速度分解成几个内在模式功能(IMF)。此外,使用Hilbert变换获得实际信号的瞬时频率和瞬时幅度和IMF信号的虚部。可以调查沿两个和三个维度的故障特征,提供更全面的信息,以帮助转子的故障诊断。实验结果对油膜振荡,油旋转,转子的双稳态,松动和转子摩擦复合故障表明了该方法的有效性。

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