EWT和ICA联合降噪在轴承故障诊断中的应用

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (16) : 42-48.

论文

吕跃刚，何洋洋

作者信息 +

Application of an EWT-ICA combined method in fault diagnosis of rolling bearings

LV Yuegang，HE Yangyang

Author information +

文章历史 +

摘要

在强背景噪声下提取非平稳振动信号的微弱故障特征时，系统信号和干扰噪声在频带互相混叠难以分离，传统消噪方法存在较大局限。为此提出一种基于经验小波变换（EWT）和独立分量分析（ICA）联合降噪的方法。首先，利用EWT算法将振动信号进行分解，避免了经验模态分解（EMD）和总体经验模态分解（EEMD）存在的模态混叠和端点效应；然后，依据峭度及相关系数准则选取相应分量，引入虚拟噪声通道；最后，利用ICA对重组信号进行解混去噪，分离出源信号后进行Hilbert包络解调，提取故障特征频率，实现故障诊断。通过对实际轴承信号的分析，验证了该方法不仅对时变、非平稳的强噪声干扰具有较好的消除效果，还能更清晰、准确地提取故障特征信息。

Abstract

When extracting the weak fault features of non-stationary vibration signals in a strong background noise environment, it was difficult to separate the system signals and the interference noises in the frequency band.The traditional denoising methods had great limitations.For this reason, we proposed a method based on the combination of the empirical wavelet transform (EWT) and the independent component analysis (ICA) to reduce noise.First, EWT was used to decompose the vibration signal, which avoids the modal aliasing and end-point effects of the empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD).Then, according to the criterion of kurtosis and correlation coefficient, the corresponding IMFs were selected and the virtual noise channel was introduced.Finally, the reconstructed signal was demixed and denoised using the ICA, and the separated source signal was analyzed by the Hilbert envelope spectrum.The fault feature frequencies were extracted to realize fault diagnosis.Through the analysis of actual bearing signals, it was verified that this method has good filtering effect on time-varying and non-stationary strong background noise, and can extract the fault feature information more clearly and accurately.

导出引用

吕跃刚，何洋洋. EWT和ICA联合降噪在轴承故障诊断中的应用[J]. 振动与冲击, 2019, 38(16): 42-48

LV Yuegang，HE Yangyang. Application of an EWT-ICA combined method in fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2019, 38(16): 42-48

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