摘要
针对滚动轴承故障振动信号非平稳的特征,以及传统傅里叶变换不能反映信号细节的缺陷,引入了一种基于本征模态函数包络谱的方法。首先,采用经验模态分解(empirical mode decomposition,EMD)将滚动轴承故障振动信号分解成若干个本征模态函数(intrinsicmode function,IMF)之和;然后,求出包含主要信息成分的IMF分量的Hilbert包络谱;最后,对照滚动轴承故障特征频率,进而判定故障类型。通过对滚动轴承内圈、外圈故障振动信号的分析处理,表明该方法能有效地提取滚动轴承的故障特征。
According to the non-stationary characteristics limitation of traditional Fourier transform unable to reflect the of the rolling bearing fault vibration signals and the details of signal, a fault diagnosis method based on envelope spectrum of intrinsic mode function was introduced. First, rolling bearing fault vibration signals were decomposed into a finite number of intrinsic mode functions （IMFs） using empirical mode decomposition （EMD） ; then, the envelope spectra of some IMFs including the main fault information were calculated ; finally, fault patterns were identified by contrast with characteristic defect frequencies of rolling bearing. Based on processing and analysis of the roiling bearing vibration signals with inner race and out race fault, the result shows that this method can extract rolling bearing fault characteristics effectively.
出处
《机电一体化》
2012年第7期60-64,共5页
Mechatronics
基金
国家科技重大专项(2009ZX0414-103)
上海市经信委引进技术的吸收与创新项目(11XI-07)
