基于EEMD-JADE的滚动轴承故障诊断分析
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摘要
滚动轴承故障信号特征往往受背景噪声影响而难以准确提取,集合经验模式分解能将源信号有效分解出具有真实物理意义的本征模态分量,提高故障特征的诊断精度,盲源分离技术能够分离故障信号进而提取故障特征。将集合经验模态分解与盲源分离技术相结合,通过相关系数的计算和敏感因子的数值判断合理选用源信号的分量,构建出噪声信号,再通过盲源分离技术,分离噪声信号。仿真分析和实验表明,此方法可以成功的分离出典型的轴承故障特征,可有效提高轴承故障诊断效果。
The characteristics of rolling bearing fault signals are often affected by background noise and difficult to be accurately extracted. Ensemble empirical mode decomposition( EEMD) can effectively decompose the source signals into the intrinsic mode function( IMF) with real physical significance and improve the diagnosis accuracy of fault characteristics. Blind Source Separation( BSS) can separate the fault signals and then extract the fault characteristics. Based on the combination of EEMD and BSS,the noise signal is constructed by selecting the component of source signal through the calculation of correlation coefficient and the judgment of sensitivity factor,and then the noise signal is separated by BSS. The simulation analysis and experiments show that this method can successfully decompose the typical bearing fault characteristics and effectively improve the bearing fault diagnosis effect.
The characteristics of rolling bearing fault signals are often affected by background noise and difficult to be accurately extracted. Ensemble empirical mode decomposition( EEMD) can effectively decompose the source signals into the intrinsic mode function( IMF) with real physical significance and improve the diagnosis accuracy of fault characteristics. Blind Source Separation( BSS) can separate the fault signals and then extract the fault characteristics. Based on the combination of EEMD and BSS,the noise signal is constructed by selecting the component of source signal through the calculation of correlation coefficient and the judgment of sensitivity factor,and then the noise signal is separated by BSS. The simulation analysis and experiments show that this method can successfully decompose the typical bearing fault characteristics and effectively improve the bearing fault diagnosis effect.
引文
[1]刘永斌.基于非线性信号分析的滚动轴承状态监测诊断研究[D].北京:中国科学技术大学,2011.
[2] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings A,1998,454(1971):903-995.
[3]程军圣,马兴伟,杨宇.基于VPMCD和EMD的齿轮故障诊断方法[J].振动与冲击,2013,32(20):9-13
[4]王志坚,韩振南,刘邱祖,等.基于MED-EEMD的滚动轴承微弱故障特征提取[J].农业工程学报,2014,30(23):70-78.
[5] Babu T R,Srikanth S,Sekhar A S. Hilbert-Huang transform for detection and monitoring of crack in a transient rotor[J]. Mechanical Systems&Signal Processing,2008,22(4):905-914.
[6]胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,31(4):429-434.
[7] Cardoso J F,Souloumiac A. Blind Beamforming for non Gaussian Signals[J].IEE Proc.-F,1993,140(6):362-370.
[8] Cardoso J F.High-order contrasts for independent component analysis[J].Neural Computation,1999,11(1):157.
[9] Cardoso J F,Souloumiac A. An efficient technique for the blind separation of complex sources[C].IEEE Signal Processing Workshop on Higher-order Statistics,1993.
[10]刘方,沈长青,何清波,等.基于时域多普勒校正和EEMD的列车轴承道旁声音监测故障诊断方法研究[J].振动与冲击,2013,32(24):104-109.
[11] Song X,Ta D,Wang W. Analysis of superimposed ultrasonic guided waves in long bones by the joint approximate diagonalization of eigen-matrices algorithm[J]. Ultrasound in Medicine&Biology,2011,37(10):1704-1713.
[12] Peng,Z. K,Peter W,et al. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechanical Systems&Signal Processing,2005,19(5):974-988.
[13]凯斯西储大学(CWRU)轴承实验数据[EB/OL].http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[2] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings A,1998,454(1971):903-995.
[3]程军圣,马兴伟,杨宇.基于VPMCD和EMD的齿轮故障诊断方法[J].振动与冲击,2013,32(20):9-13
[4]王志坚,韩振南,刘邱祖,等.基于MED-EEMD的滚动轴承微弱故障特征提取[J].农业工程学报,2014,30(23):70-78.
[5] Babu T R,Srikanth S,Sekhar A S. Hilbert-Huang transform for detection and monitoring of crack in a transient rotor[J]. Mechanical Systems&Signal Processing,2008,22(4):905-914.
[6]胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,31(4):429-434.
[7] Cardoso J F,Souloumiac A. Blind Beamforming for non Gaussian Signals[J].IEE Proc.-F,1993,140(6):362-370.
[8] Cardoso J F.High-order contrasts for independent component analysis[J].Neural Computation,1999,11(1):157.
[9] Cardoso J F,Souloumiac A. An efficient technique for the blind separation of complex sources[C].IEEE Signal Processing Workshop on Higher-order Statistics,1993.
[10]刘方,沈长青,何清波,等.基于时域多普勒校正和EEMD的列车轴承道旁声音监测故障诊断方法研究[J].振动与冲击,2013,32(24):104-109.
[11] Song X,Ta D,Wang W. Analysis of superimposed ultrasonic guided waves in long bones by the joint approximate diagonalization of eigen-matrices algorithm[J]. Ultrasound in Medicine&Biology,2011,37(10):1704-1713.
[12] Peng,Z. K,Peter W,et al. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechanical Systems&Signal Processing,2005,19(5):974-988.
[13]凯斯西储大学(CWRU)轴承实验数据[EB/OL].http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.