张俊甲,马增强,王梦奇,阮婉莹.基于VMD与自相关分析的滚动轴承故障特征提取[J].电子测量与仪器学报,2017,31(9):1372-1378
基于VMD与自相关分析的滚动轴承故障特征提取
Rolling bearing fault feature extraction based on VMD and autocorrelation analysis
  
DOI:10.13382/j.jemi.2017.09.004
中文关键词:  自相关分析  变分模态分解  降噪  滚动轴承  故障诊断
英文关键词:autocorrelation analysis  variational mode decomposition (VMD)  noise reduction  rolling bearing  fault diagnosis
基金项目:国家自然科学基金(U1534204,11372199,11572206)、河北省然科学基金(A2014210142)资助项目
作者单位
张俊甲 石家庄铁道大学电气与电子工程学院石家庄050043 
马增强 石家庄铁道大学电气与电子工程学院石家庄050043 
王梦奇 石家庄铁道大学电气与电子工程学院石家庄050043 
阮婉莹 石家庄铁道大学电气与电子工程学院石家庄050043 
AuthorInstitution
Zhang Junjia School of Electronical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
Ma Zengqiang School of Electronical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
Wang Mengqi School of Electronical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
Ruan Wanying School of Electronical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
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中文摘要:
      滚动轴承故障信号多呈现非平稳、多分量调制特性,早期故障信号调制特性微弱、易受周围设备噪声干扰,导致轴承早期故障特征淹没在噪声信号中,故障特征难以提取。为此,提出一种变分模态分解(variational mode decomposition, VMD)与自相关分析相结合的轴承故障特征提取方法。首先利用自相关分析消除故障信号中噪声干扰,提取周期成分;然后再用VMD算法将消噪信号分解成若干本征模态分量(intrinsic mode function, IMF),运用能量算子对相关系数及峭度值较大分量进行解调分析;最后通过能量解调谱来判别滚动轴承故障类型。将该方法应用到滚动轴承仿真故障数据和实测数据中,结果表明,该方法可降低了噪声的干扰,有效提取故障特征频率,能够实现滚动轴承故障的精确诊断。
英文摘要:
      Early fault signal of rolling bearing usually presents non stationary multi component characteristics, early fault features of bearing submerged in the back ground noise are difficult to identify because of the weak modulated characteristics and strong noise. Therefore, the fault diagnosis method based on variational mode decomposition (VMD) and autocorrelation analysis was proposed.At first, the noise was eliminated and the periodic components in signals were extracted by using autocorrelation analysis. Then VMD was used to decompose the denoised signal into many intrinsic mode functions and the IMFs of the biggest coefficient and kurtosis was selected and demodulated with Teager energy operator. At last, the bearing fault type was distinguished through the energy spectrum. The simulation experiments and practical engineering experiments have been carried out and the results show that this method is able to reduce the interference of noise and extract effectively the fault feature frequency, and realize accurate diagnosis for rolling bearing fault.
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