应用EMD和双谱分析的故障特征提取方法
Feature Extraction Method Based on Empirical Mode Decomposition and Bispectrum Analysis

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。
In view of the deficiency of traditional bispectrum that has good insensitivity to independent Gaussian noise but is incapable of avoiding the disturbance of non-Gaussian noise, a new feature extraction method based on EMD and bispectrum analysis is proposed. Firstly, decompose the signal by EMD. Then, energy-related method is applied to removal the illusive intrinsic mode functions (IMFs) of EMD. Finally, bispectrum analysis is applied to the real IMFs to extract the fault feature. The results of experiment and practical signals analysis show that the proposed method is feasible and effective for extracting feature of rolling bearing, and it is more effective compared with traditional power spectral analysis and traditional bispectrum analysis.