基于遗传算法的VMD参数优化与小波阈值的轴承振动信号去噪分析
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- 英文论文题名：Denoising analysis of bearing vibration signal based on genetic algorithm and wavelet threshold VMD
- 论文作者：刘嘉敏 ; 彭玲 ; 刘军委 ; 袁佳成
- 英文论文作者：LIU Jia-min ; PENG-Ling ; LIU Jun-wei ; YUAN Jia-cheng ; Key Laboratory of Optoelectronic Technology and Systems Ministry of Education ; Chongqing University
- 年：2016
- 作者机构：重庆大学光电技术与系统教育部重点实验室;
- 论文关键词：遗传算法 ; 变分模态分解 ; 小波阈值 ; 去噪
- 英文论文关键词：Genetic algorithm ; variational mode decomposition ; wavelet threshold ; denoising
- 会议召开时间：2016-07-28
- 会议录名称：第二十七届全国振动与噪声应用学术会议论文集
- 语种：中文
- 分类号：TH133.3
- 学会代码：ZDZD
- 会议名称：第二十七届全国振动与噪声应用学术会议
- 会议地点：中国黑龙江哈尔滨
- 主办单位：中国振动工程学会振动与噪声控制专业委员会
- 学会名称：中国振动工程学会振动与噪声控制专业委员会
- 页数：9
- 文件大小：1469k
- 原文格式：D
- 会议级别：全国
摘要
针对轴承振动信号夹杂的噪声极大影响有用信息的提取,提出了基于遗传算法的变分模态分解(variational mode decomposition,VMD)与小波阈值去噪方法。该方法首先利用遗传算法选择合适的VMD参数,然后用VMD方法对含噪声的信号进行自适应分解,最后对分解的模态分别进行小波阈值处理后重构信号,得到去噪后的信号。在实际轴承信号的实验结果表明,该方法与常用的去噪方法相比,得到能够得到更高的信噪比和更低的均方差。
Aiming at the extraction of useful information from the noise of bearing vibration signal, the mode decomposition variational(VMD) and wavelet threshold denoising method based on genetic algorithm is proposed. The method firstly utilizes genetic algorithm selecting appropriate parameters of the VMD, then the noise signal is decomposed adaptively by VMD method, finally processing the modes of decomposition respectively by wavelet thresholding method, restructuring the signal to get denoised signal. Experimental results on actual bearing signals show that the proposed method can obtain higher signal-to-noise ratio and lower mean square deviation compared with several common denoising methods.
Aiming at the extraction of useful information from the noise of bearing vibration signal, the mode decomposition variational(VMD) and wavelet threshold denoising method based on genetic algorithm is proposed. The method firstly utilizes genetic algorithm selecting appropriate parameters of the VMD, then the noise signal is decomposed adaptively by VMD method, finally processing the modes of decomposition respectively by wavelet thresholding method, restructuring the signal to get denoised signal. Experimental results on actual bearing signals show that the proposed method can obtain higher signal-to-noise ratio and lower mean square deviation compared with several common denoising methods.
引文
[1]吕勇,李友荣,肖涵.基于降噪及独立分量分析的轴承故障声信号特征提取[J].武汉科技大学学报,2008,31(1):91-94.
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[14]包广清,常勇,杨国金.基于EMD阈值方法的轴承故障振动信号去噪[J].计算机工程与应用,2015,51(10):205-210.
[15]蔡剑华,胡惟文,王先春.基于EMD-ICA去噪的滚动轴承故障诊断方法[J].机械设计,2015,32(1):17-23.
[16]艾延延,冯岩岩,周海仑.LMD和改进小波阈值去噪的轴承声发射信号故障诊断[J].科学技术与工程,2014,33(14):86-92.
[17]Loparo K A.Bearings vibration data set,case western reserve university[J].2005-02-01].http://www.eecs.cwru.edu/laboratory/bearing/download.htm,2003.
[2]汪泽明.齿轮及滚动轴承故障特征分离方法的研究[D].太原:太原理工大学,2010.
[3]张贤达,保铮.非平稳信号分析与处理[M].3版.北京:国防工业出版社,2001.
[4]Abbasion S,Rafsanjani A,Farshidianfar A,et al.Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine[J].Mechanical Systems and Signal Processing,2007,21(7):2933-2945.
[5]Wang X,Zi Y,He Z.Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2011,25(1):285-304.
[6]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[C]//Proceedings of the Royal Society of London A:Mathematical,Physical and Engineering Sciences.The Royal Society,1998,454(1971):903-995.
[7]Kopsinis Y,Mc Laughlin S.Empirical mode decomposition based denoising techniques[J].1st International Work-shop on Cognitive Information Processing(CIP),2008.
[8]蔡剑华,胡惟文,王先春.基于EMD-ICA去噪的滚动轴承故障诊断方法[J].机械设计,2015,32(1):17-23.
[9]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].Signal Processing,IEEE Transactions on,2014,62(3):531-544.
[10]Dragomiretskiy K,Zosso D.Two-dimensional variational mode decomposition[C]//Energy Minimization Methods in Computer Vision and Pattern Recognition.Springer International Publishing,2015:197-208.
[11]Nocedal,J.,Wright,S.J.:Numerical optimization,2nd edn.Springer,Berlin(2006).
[12]Donoho D L.De-noising by soft-thresholding[J].Information Theory,IEEE Transactions on,1995,41(3):613-627.
[13]Houck C R,Joines J,Kay M G.A genetic algorithm for function optimization:a Matlab implementation[J].NCSU-IE TR,1995,95(09).
[14]包广清,常勇,杨国金.基于EMD阈值方法的轴承故障振动信号去噪[J].计算机工程与应用,2015,51(10):205-210.
[15]蔡剑华,胡惟文,王先春.基于EMD-ICA去噪的滚动轴承故障诊断方法[J].机械设计,2015,32(1):17-23.
[16]艾延延,冯岩岩,周海仑.LMD和改进小波阈值去噪的轴承声发射信号故障诊断[J].科学技术与工程,2014,33(14):86-92.
[17]Loparo K A.Bearings vibration data set,case western reserve university[J].2005-02-01].http://www.eecs.cwru.edu/laboratory/bearing/download.htm,2003.