基于EEMD自适应选取IMF的机泵滚动轴承故障诊断
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摘要
滚动轴承是机泵中关键的旋转零部件,研究其有效的故障状态监测及诊断方法对机泵的稳定运行具有重要的意义。总体平均经验模态分解方法利用高斯白噪声具有频率均匀分布的统计特性,将其加入到信号的EMD分解中,促进抗混分解,避免了用EMD方法因IMF分量的不连续性而造成的模态混淆现象。首先利用EEMD方法把机泵滚动轴承故障信号分解成若干个内禀模态函数(IMFs)之和,然后用文中提出的自适应选取IMF分量的方法,对自适应选取的IMF分量进行能量算子解调,提取故障特征,从而避免了基于人为经验选取IMF分量进行能量算子解调所造成的主观性及不科学性。
As the key rotating component in pumb, it is very meaningful to study the effective status monitoring and fault diagnosis method of rolling element bearing. Gaussian white noise has the statistical property of uniform distribution in frequency range. Using this property, Gaussian white noise is added into the complex signal every time when it is decomposed by EMD method, so that mode mixing phenomenon induced by IMF's discontinuity can be avoided, because gaussian white noise plays the role of smooth function.This process is called ensemble empirical mode decomposition(EEMD). Firstly, EEMD method is used to decompose a multi-compo-nent AM-FM signal into a number of IMFs. Secondly, energy operator demodulation method is applied to the automatic selected IMF proposed by the paper to extract fault feature. Automatic selection of IMF can make up the subjectivity and unscientific of selection IMF for further calculation done on the basis of the experience of user.
As the key rotating component in pumb, it is very meaningful to study the effective status monitoring and fault diagnosis method of rolling element bearing. Gaussian white noise has the statistical property of uniform distribution in frequency range. Using this property, Gaussian white noise is added into the complex signal every time when it is decomposed by EMD method, so that mode mixing phenomenon induced by IMF's discontinuity can be avoided, because gaussian white noise plays the role of smooth function.This process is called ensemble empirical mode decomposition(EEMD). Firstly, EEMD method is used to decompose a multi-compo-nent AM-FM signal into a number of IMFs. Secondly, energy operator demodulation method is applied to the automatic selected IMF proposed by the paper to extract fault feature. Automatic selection of IMF can make up the subjectivity and unscientific of selection IMF for further calculation done on the basis of the experience of user.
引文
[1]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis[J].Proceedings of the Royal Society,1998(3):903-995.
[2]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112-114.
[3]LEI Yaguo,HE Zhengjia,ZI Yanyang.Application of the EEMDmethod to rotor fault diagnosis of rotating machinery[J].Mechanical Systems and Signal,2009,23(4):1327-1338.
[4]WU Zhaohua,HUANG N E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proc R Soc Lond A,2004,11(6):1597-1611.
[5]WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Advance in Adaptive Data Analysis,2009(1):1-41.
[6]HENG J,YU D,YANG Y.Energy operator demodulating approach based on EMD and its application to mechanical fault diagnosis[J].Chinese Journal of Mechanical Engineering,2004,40(8):115-118.
[7]WEI Z,MA B,YAO Z,et al.Feature extraction of gas valve fault using wavelet packet transform and energy operator[J].Journal of Vibration,Measurement and Diagnosis,2011,31(1):50-54.
[8]PETROS M,JAMES F K,THOMAS F Q.On amplitude and frequency demodulation using energy operator[J].IEEE Transactions on Signal Processing,1993,41(4):1532-1550.
[9]DWYER R F.Detection of non-gaussian signals by frequency domain kurtosis estimation[J].Boston:International Conference on Acoustics,Speech,and Signal Processing,1983:607-610.
[10]ANTONI J.Fast computation of the kurtogram for the detection of transient fault[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[11]ANTONI J.The spectral kurtosis:A useful tool for characterizing non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[2]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112-114.
[3]LEI Yaguo,HE Zhengjia,ZI Yanyang.Application of the EEMDmethod to rotor fault diagnosis of rotating machinery[J].Mechanical Systems and Signal,2009,23(4):1327-1338.
[4]WU Zhaohua,HUANG N E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proc R Soc Lond A,2004,11(6):1597-1611.
[5]WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Advance in Adaptive Data Analysis,2009(1):1-41.
[6]HENG J,YU D,YANG Y.Energy operator demodulating approach based on EMD and its application to mechanical fault diagnosis[J].Chinese Journal of Mechanical Engineering,2004,40(8):115-118.
[7]WEI Z,MA B,YAO Z,et al.Feature extraction of gas valve fault using wavelet packet transform and energy operator[J].Journal of Vibration,Measurement and Diagnosis,2011,31(1):50-54.
[8]PETROS M,JAMES F K,THOMAS F Q.On amplitude and frequency demodulation using energy operator[J].IEEE Transactions on Signal Processing,1993,41(4):1532-1550.
[9]DWYER R F.Detection of non-gaussian signals by frequency domain kurtosis estimation[J].Boston:International Conference on Acoustics,Speech,and Signal Processing,1983:607-610.
[10]ANTONI J.Fast computation of the kurtogram for the detection of transient fault[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[11]ANTONI J.The spectral kurtosis:A useful tool for characterizing non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.