Application of multi-scale entropy correlation optimization to mode decomposition in fault diagnosis under variational framework

doi:10.16731/j.cnki.1671-3133.2017.04.027

Abstract

Abstract: According to optimal mode selection and key parameter identification of a new adaptive mode decomposition under variational framework Variational Mode Decomposition (VMD),from the idea of binary search,the multi-scale entropy correlation and correlation coefficient in Fourier domain are presented to solve the problem above for VMD,and its filtering essence is revealed through decomposition characteristics of bearing fault simulation signal in Fourier domain.With analysis for simulation signal and engineering application of bearing fault,the results show that,compared with Empirical Mode Decomposition(EMD) and Ensemble Empirical Mode Decomposition(EEMD),de-noising effect of the Improved VMD(IVMD) is more obvious,which is an effective adaptive mode decomposition method in Fourier domain,and can extract the weak feature frequency of fault signal more accurately,achieve correct recognition of bearing fault.

Key words: variational, optimal mode, parameter identification, fault diagnosis, multi-scale entropy correlation coefficient

CLC Number:

Li Qinxue, Zhang Qinghua, Cui Delong, Shu Lei, Huang Jianfeng. Application of multi-scale entropy correlation optimization to mode decomposition in fault diagnosis under variational framework[J]. Modern Manufacturing Engineering, 2017, 439(4): 142-148.

References

[1] 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 of the Royal Society A,1998(454):903-993.
[2] WU Z H,HUANG N E.Ensemble Empirical Mode Decomposition-A Noise Assisted Data Analysis Method [J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[3] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE transactions on signal processing,2014,62(3):531-544.
[4] LAHMIRI S.Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis[J].Physica A:Statistical Mechanics and its Applications,2015(437):130-138.
[5] UPADHYAY A,PACHORI R B.Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition[J].Journal of the Franklin Institute,2015,352(7):2679-2707.
[6] 唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-81.
[7] WANG Y,MARKERT R,XIANG J,et al.Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J].Mechanical Systems and Signal Processing,2015(60):243-251.
[8] 蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J].振动工程学报,2010,23(4):430-437.
[9] RICHMAN J S,MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology-Heart and Circulatory Physiology,2000,278(6):H2039-H2049.
[10] COSTA M,GOLDBERGER A L,PENG C K.Multiscale entropy analysis of biological signals[J].Physical review E,2005,71(2):021906.
[11] COSTA M,GOLDBERGER A L,PENG C K.Multiscale Entropy Analysis of Complex Physiologic Time Series[J].Physical Review Letters,2002,89(6):1-18.
[12] LIU H,HAN M.A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings[J].Mechanism and Machine Theory,2014(75):67-78.
[13] LOPARO K A.Western Reserve University Bearing Data Center Website[DB/OL].http://csegroups.case.edu/bearingdatacenter/home,2012-04-10.

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