应用EMD和双谱分析的故障特征提取方法

Aiming at the deficiency that traditional bispectral analysis can theoretically only suppress Gaussian noise but is powerless against non - Gaussian noise, a fault feature extraction method using empirical mode decomposition (EMD for short) and bispectral analysis is proposed and applied to the fault diagnosis of rolling bearings. Firstly, the signal is decomposed by EMD; secondly, the pseudo - intrinsic mode function (IMF for short) emerging in the EMD decomposition process is removed by using the energy - related method; finally, the bispectral analysis is carried out on the obtained real IMF to extract the fault features. The simulation and experimental results show that the proposed method is superior to power spectral analysis and traditional bispectral analysis, and can more effectively extract the mechanical fault feature information under a strong noise background, providing a new method for the fault feature extraction of rolling bearings.

针对传统双谱分析从理论上仅能抑制高斯噪声，但对非高斯噪声无能为力的不足，提出了一种利用经验模式分解（（empirical mode decomposition, 简称EMD）和双谱分析的故障特征提取方法，并应用于滚动轴承故障诊断中。首先，对信号进行EMD分解；其次，利用能量相关法去除EMD分解过程中出现的伪本征模态分量（intrinsic mode function, 简称IMF）；最后，对得到的真实IMF进行双谱分析提取故障特征。仿真和实验结果表明，所提出的方法优于功率谱分析和传统双谱分析，能够更有效地提取强噪声背景下的机械故障特征信息，为滚动轴承的故障特征提取提供了一种新的方法。