基于谱峭度与变分模态分解的转子微弱不对中故障诊断

针对噪声干扰下转子微弱不对中故障特征难以提取的问题,提出一种谱峭度与变分模态分解的转子故障诊断方法。该方法首先利用谱峭度(Spectral Kurtosis)滤除信号背景噪声以强化故障特征相关信号分量,然后通过变分模态分解(Variational Mode Decomposition,VMD)将转子振动信号分解为一系列本征模态分量并对各分量进行频谱分析,提取转子的故障特征。将该方法应用到转子不对中故障实验数据中,结果表明,该方法能有效提取出转子微弱不对中故障特征,并且结果要优于基于谱峭度与经验模态分解(EMD)方法的分析结果。

Fault Diagnosis ofWeak Misalignment of Rotors Based on Spectral Kurtosis and VMD

In order to solve the problem that the fault features of the rotors in weak misalignment under the noise interference are difficult to extract, a rotor fault diagnosis method was proposed based on Spectral Kurtosis and variational mode decomposition. Firstly, Spectral Kurtosis was used to filter out the signal background noise to enhance the fault characteristic correlation signal component. Then VMD (variational mode decomposition) was used to decompose the signal into IMFs (Intrinsic Mode Function); Finally the frequency spectrum of the IMFs were calculated to extract the fault feature of the rotor. The proposed method was applied in misalignment of rotors. The results showed that the proposed diagnosis method can more effectively and accurately extract the weak misalignment of rotor, superior to the diagnosis method based on Spectral Kurtosis and empirical mode decomposition (EMD).