马增强,李亚超,阮婉莹,张安.基于变分模态分解和谱峭度法的滚动轴承故障特征提取[J].电子测量与仪器学报,2017,31(11):1782-1787
基于变分模态分解和谱峭度法的滚动轴承故障特征提取
Rolling bearing fault feature extraction based on VMD and spectral kurtosis
  
DOI:10.13382/j.jemi.2017.11.013
中文关键词:  故障诊断  滚动轴承  变分模态分解  谱峭度
英文关键词:fault diagnosis  rolling bearing  VMD  spectral kurtosis
基金项目:国家自然科学基金(11227201,11372199)、河北省自然科学基金(A2014210142)资助项目
作者单位
马增强 石家庄铁道大学电气与电子工程学院石家庄050043 
李亚超 1.石家庄铁道大学电气与电子工程学院石家庄050043;2.南车青岛四方机车车辆股份有限公司青岛266111 
阮婉莹 石家庄铁道大学电气与电子工程学院石家庄050043 
张安 石家庄铁道大学电气与电子工程学院石家庄050043 
AuthorInstitution
Ma Zengqiang School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
Li Yachao 1.School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 2.CRRC Qingdao Sifang Co.Ltd., Qingdao 266111, China 
Ruan Wanying School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
Zhang An School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 
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中文摘要:
      针对共振解调中带通滤波器参数的选取通常比较困难,以及滚动轴承早期微弱故障信号通常被强烈的背景噪声淹没,为此,提出了使变分模态分解(variational mode decomposition, VMD)和谱峭度法共同作用来处理故障信号的方法。首先要重构故障信号,利用VMD分解得到故障信号的本征模态分量(intrinsic mode function, IMF),再计算各分量对应的峭度值对其自适应重构。然后,对重构信号进行快速谱峭度分析,并据此设计带通滤波器。最后,根据重构信号共振解调后的谱线即可准确判断轴承故障。通过处理实测数据进行诊断,结果表明了该方法较传统共振解调法诊断结果更精确。由此可见,谱峭度法在滤波器参数选择上具有可靠性,以及VMD与谱峭度结合能够降低噪声干扰提取微弱故障信号。
英文摘要:
      In order to solve the problem that the band pass filter parameters in resonance demodulation are difficult to select and the fault signals of rolling bearing in early failure period aredrowned in strong background noise,thefault diagnosis methodcombining variational mode decomposition (VMD)withspectral kurtosis is proposed. Firstly, the fault signals need to bereconstructed self adaptively, so several intrinsic mode function (IMF) are obtained by VMD, and adaptive reconstruction is performed by computing the kurtosis of IMFs. Next,we can analyze the reconstructed signals by spectral kurtosis and design the band pass filter. Finally, the working status of rolling bearingis identified through the resonance demodulation spectrum of reconstructedsignal. By processing real data,the results show that the method is more accurate than traditional resonance demodulation in diagnosing the fault of rolling bearing. Thus, it can be seen, the spectral kurtosis is reliable in selection of the filter parameters, and combining VMD and spectral kurtosis can reduce the noise and extract weak fault signal.
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