变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用

doi:10.16731/j.cnki.1671-3133.2017.04.027

现代制造工程 ›› 2017, Vol. 439 ›› Issue (4): 142-148.doi: 10.16731/j.cnki.1671-3133.2017.04.027

• 设备设计/诊断维修/再制造 • 上一篇下一篇

变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用

李沁雪^1,2,3, 张清华², 崔得龙^1,2, 舒磊², 黄剑锋²

1 广东石油化工学院计算机与电子信息学院,茂名 525000
2 广东石油化工学院广东省石化装备故障诊断重点实验室,茂名 525000
3 华南理工大学自动化科学与工程学院,广州 510640

收稿日期:2015-12-15 出版日期:2017-04-18 发布日期:2018-01-09
作者简介:李沁雪,讲师、在读博士,主要研究方向为智能信息处理、故障诊断等研究,已发表论文14篇。
张清华,校长,教授,博士生导师,主要研究方向为故障诊断、智能控制等研究。
E-mail:liqinxue512@foxmail.com
基金资助:
国家自然科学基金项目(61174113,61672174);广东省自然科学基金项目(2016A030307029)

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

Li Qinxue^1,2,3, Zhang Qinghua², Cui Delong^1,2, Shu Lei², Huang Jianfeng²

1 School of Computer and Electronic Information,Guangdong University of Petrochemical Technology,Maoming 525000,Guangdong,China
2 Guangdong Provincial Key Laboratory of Petrochemical Equipment Flaut Diagnosis,Guangdong University of Petrochemical Technology,Maoming 525000,Guangdong,China
3 School of Automation Science and Engineering,South China Univ.of Tech,Guangzhou 510640,China

Received:2015-12-15 Online:2017-04-18 Published:2018-01-09

摘要/Abstract

摘要： 针对变分框架下,一种新的模态分解——变分模态分解(Variational Mode Decomposition,VMD)的最优模态分量选择和关键参数辨识问题,借鉴折半查找的思想,提出应用多尺度熵相关系数和频域相关系数来改进VMD的上述关键环节,并通过轴承故障信号仿真研究其频域分解的数据特点,揭示其滤波本质;轴承故障信号仿真及工程应用的结果表明,相对于经验模态分解(Empirical Mode Decomposition,EMD)和总体平均经验模态分解(Ensemble Empirical Mode Decomposition,EEMD),改进后的VMD(IVMD)去噪效果更为明显,是一种有效的自适应频域模态分解方法,可更为准确地提取出微弱特征频率信息,实现轴承故障的正确识别。

关键词: 变分, 最优模态, 参数辨识, 故障诊断, 多尺度熵相关系数

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

中图分类号:

李沁雪, 张清华, 崔得龙, 舒磊, 黄剑锋. 变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用[J]. 现代制造工程, 2017, 439(4): 142-148.

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.

参考文献

[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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变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用

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

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