基于二次聚类分割与Teager能量谱的滚动轴承微弱故障特征提取

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (13) : 246-253.

论文

王望望1，邓林峰1，2，赵荣珍1，张爱华2

作者信息 +

Weak fault feature extraction of rolling bearing based on secondary clustering segmentation and Teager energy spectrum

WANG Wangwang1, DENG Linfeng1,2, ZHAO Rongzhen1, ZHANG Aihua2

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文章历史 +

摘要

如何从含噪振动信号中准确提取微弱周期性故障特征是辨识滚动轴承局部故障的关键。针对此问题，提出一种基于二次聚类分割与Teager能量谱的滚动轴承微弱故障特征提取方法。首先通过傅里叶变换得到故障信号的频谱并利用模糊C均值算法对其进行聚类分割；然后对每个频段进行傅里叶逆变换并计算不同频段时域信号的峭度，选取峭度最大频段对应的时域信号作为初次滤波信号，对该信号进行第二次聚类分割及傅里叶逆变换，选取最大峭度对应的频段作为通带过滤信号，进一步消除噪声和自然周期性成分的影响；最后采用Teager能量算子对得到的时域故障信号进行解调分析，以获取滚动轴承微弱故障特征频率。仿真分析和实验验证结果表明，该方法能准确有效地提取出滚动轴承微弱故障特征。

Abstract

The key to identify local faults of rolling bearing is to accurately extract weak periodic fault features from noisy vibration signals. Aiming at this problem, a method to extract weak fault features of rolling bearing based on secondary clustering segmentation and Teager energy spectrum was proposed here. Firstly, the frequency spectrum of a fault signal was obtained with Fourier transform, and the clustering segmentation was done for this spectrum using the fuzzy C-means algorithm. Then, the inverse Fourier transform was done for each frequency segment to calculate kurtosis values of time domain signals in different frequency bands, and the time domain signal corresponding to the frequency band with the maximum kurtosis was selected as the useful signal to be further filtered. The secondary clustering segmentation and the inverse Fourier transform were done for the useful signal,so the frequency band with the maximum kurtosis was taken as the passband singal to further eliminate effects of noise and natural periodic components. Finally, Teager energy operator was used to demodulate the obtained time domain fault signal to acquire feature frequencies of bearing weak faults. Simulation analysis and test verification results showed that the proposed method can accurately and effectively extract weak fault features of rolling bearing.

关键词

二次聚类分割 / Teager能量谱 / 峭度 / 滚动轴承 / 故障特征提取

Key words

secondary clustering segmentation

/ Teager energy spectrum / kurtosis / rolling bearing / fault feature extraction

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王望望1，邓林峰1，2，赵荣珍1，张爱华2. 基于二次聚类分割与Teager能量谱的滚动轴承微弱故障特征提取[J]. 振动与冲击, 2020, 39(13): 246-253

WANG Wangwang1, DENG Linfeng1,2, ZHAO Rongzhen1, ZHANG Aihua2. Weak fault feature extraction of rolling bearing based on secondary clustering segmentation and Teager energy spectrum[J]. Journal of Vibration and Shock, 2020, 39(13): 246-253

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