变分模态分解在转子故障诊断中的应用
Application of Variational Mode Decomposition in Fault Diagnosis of Rotors

针对转子故障诊断问题，提出一种基于变分模态分解(variational mode decomposition，简称VMD)的信号处理方法。该方法在获取分解分量的过程中通过迭代搜寻变分模型最优解来确定每个分量的频率中心及带宽，从而能够自适应地实现信号的频域剖分及各分量的有效分离，对各单分量信号进行希尔伯特变换，即可得到瞬时的频率和幅值信息。对仿真信号和典型转子故障信号进行VMD方法和经验模态分解（empirical mode decomposition, 简称EMD）方法的分析比较，以验证所提方法的有效性。仿真信号的分解结果表明，变分模态能够准确分离出信号中的固有模态分量且不存在模态混叠；转子故障实验信号的分析结果表明，所提方法能够有效提取出明显的故障特征，从而准确诊断出转子存在的故障。
A signal processing method is proposed for rotor fault diagnosis problem based on variational mode decomposition(VMD). The center frequency and bandwidth of each component are determined in the process of the obtainment of the components by iteratively searching the optimal solution of variational model. Thus the signal frequency domain is adaptively subdivided and effectively separated from each component. The signal instantaneous frequency and amplitude of each single component can be obtained by Hilbert transform. To verify the effectiveness of the proposed method, a comparison is made to show the performance of VMD and empirical mode decomposition(EMD) in the analysis of simulation signal and some typical rotor fault signals. The decomposition results of simulation signal show that the VMD can accurately decompose the intrinsic modes and have no modal mixing. Rotor fault experiment signal analysis results show that the proposed method can effectively extract the evident fault features, so as to accurately diagnose the rotor fault.