基于VMD和Infogram的滚动轴承故障特征提取

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摘要带通滤波器参数（中心频率和带宽）选取是共振解调的关键，针对快速峭度图找寻的中心频率偏大、带宽过宽的问题，提出Infogram（信息图）用于确定滤波器参数；并利用变分模态分解（Variational Mode Decomoposition, VMD）预先对信号进行重构，以减少噪声对信息图的影响，增强其应用效果。首先对轴承故障振动信号进行变分模态分解得到有限个模态分量，然后根据模态选取准则确定包含故障信息较多的模态分量进行信号重构，再应用信息图确定最佳共振频带的中心频率和带宽，最后对重构信号进行带通滤波和包络谱分析，识别轴承故障特征频率。仿真分析和轴承外圈模拟故障试验验证了该方法的有效性。

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	夏均忠 1
	于明奇 1
	黄财 2
	汪治安 1
	吕麒鹏 1

关键词 ：滚动轴承, 特征提取, 快速峭度图, 变分模态分解, 信息图

Abstract：It is crucial to select the band-pass filter parameters (center frequency and bandwidth) in resonance demodulation. To overcome a problem occurred in fast kurtogram that both the center frequency and bandwidth are too large, infogram was presented to select the filter parameters. And the signal was reconstructed beforehand to reduce the influence of noise on infogram for added effect. Firstly, roller bearing fault vibration signals were decomposed into a finite number of modes. Secondly, the modes contained rich fault information were selected according to the selection criterion of modes, then the center frequency and bandwidth of optimal resonance frequency band were selected with the help of infogram. Finally, fault feature frequency were obtained by band filter and envelope demodulation. The simulated signal and the measured outer fault signal of rolling bearing show that the proposed method is effective for fault feature extraction of rolling bearing.

Key words： rolling element bearing fault feature extraction fast kurtogram VMD infogram

收稿日期: 2016-12-01 出版日期: 2017-11-15

引用本文:

夏均忠 1，于明奇 1，黄财 2，汪治安 1，吕麒鹏 1. 基于VMD和Infogram的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(22): 111-117.
XIA Jun-zhong1，YU Ming-qi1，HUANG Cai2，WANG Zhi-an1，LV Qi-peng1. Fault feature extraction of rolling element bearing based on VMD and Infogram. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(22): 111-117.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I22/111

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