基于多重分形谱参数的刀具磨损状态特征提取
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摘要
鉴于多重分形理论在精细刻画系统非线性现象和过程方面具有独特优势,提出了基于多重分形理论的刀具磨损状态特征提取方法.首先估计去噪后的刀具磨损声发射信号多重分形谱,验证其标度不变性和自相似性;然后在此基础上研究了刀具磨损多重分形谱参数α0、Δα和f(αmin)随磨损量变化情况,并通过散点图描述三种特征参数表征刀具磨损阶段的有效性;最后结合切削条件讨论特征f(αmin)与切削速度的关联性.研究结果表明:多重分形谱参数与磨损阶段之间具有较强的关联性,进而提出了基于多重分形谱参数进行刀具磨损状态特征提取的新方法.
Considering the unique advantages of multifractal theory in depicting the phenomena and processes of nonlinear systems,a method of feature extraction based on multifractal theory is proposed. To estimate the noise of the acoustic emission signal of tool wear,the multifractal spectrum,verify the scale invariance and self similarity; on the basis of the research on tool wear multifractal spectrum parameters of α0、f( αmin) and Δα with wear volume changes,and the effectiveness of the scatter diagram to describe three kinds of characteristics parameters of tool wear stage; finally,discuss the correlation characteristics of cutting conditions and cutting speed of the Δα.The results show that there is a strong correlation between multifractal spectrum parameters and wear stages.A new method for feature extraction of tool wear based on multifractal spectral parameters is proposed.
Considering the unique advantages of multifractal theory in depicting the phenomena and processes of nonlinear systems,a method of feature extraction based on multifractal theory is proposed. To estimate the noise of the acoustic emission signal of tool wear,the multifractal spectrum,verify the scale invariance and self similarity; on the basis of the research on tool wear multifractal spectrum parameters of α0、f( αmin) and Δα with wear volume changes,and the effectiveness of the scatter diagram to describe three kinds of characteristics parameters of tool wear stage; finally,discuss the correlation characteristics of cutting conditions and cutting speed of the Δα.The results show that there is a strong correlation between multifractal spectrum parameters and wear stages.A new method for feature extraction of tool wear based on multifractal spectral parameters is proposed.
引文
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[3]彭昶.基于混沌理论的刀具磨损识别研究[D].吉林:东北电力大学,2016.
[4] M.Bolgorian,R.Raei.A multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in Tehran stockmarket[J].Physica A Statistical Mechanics&Its Applications,2011,390(21/22):3815-3825.
[5]张亢,程军圣,杨宇.基于局部均值分解与形态学分形维数的滚动轴承故障诊断方法[J].振动与冲击,2013,32(9):90-94.
[6] J.H.Lim,E.J.Khang,T.H.Lee,et al.Detrended fluctuation analysis and kolmogorov–sinai entropy of electroencephalogram signals[J].Phys-ics Letters A,2013,377(38):2542-2545.
[7]刘国华,黄平捷,龚翔,等.基于分形维和独立分量分析的声发射特征提取[J].华南理工大学学报:自然科学版,2008,36(1):76-80.
[8]郑光明,赵军,徐汝锋,等.陶瓷刀具材料断口形貌及裂纹扩展的分形特征[J].功能材料,2015,46(21):21133-21136.
[9] W.Jan Kantelhardt,A.Stephan Zschiegner,Eva Koscielny-Bunde,et al.Multifractal detrended fluctuation analysis of nonstationary time series[J].Physica A:Statistical Mechanics and its Applications,2002,316(1):87-114.
[10]田再克,李洪儒,孙健,等.基于改进MF-DFA和SSM-FCM的液压泵退化状态识别方法[J].仪器仪表学报,2016,37(8):1851-1860.
[11]林近山,陈前.基于多重分形去趋势波动分析的齿轮箱故障特征提取方法[J].振动与冲击,2013,32(2):97-101.
[12]孙斌,姚海涛.风电场风速时间序列的多重分形去趋势波动分析[J].电工技术学报,2014,29(6):204-210.
[13]王卓,王天然,苑明哲,等.基于多重分形的水泥回转窑工况识别研究[J].仪器仪表学报,2009,30(4):711-716.
[14] Zhou Yu,Leung Yee,Yu Zu-Guo.Relationships of exponents in multifractal detrended fluctuation analysis and conventional multifractal anal-ysis[J].Chinese Physics B,2011,87(9):98-106.
[15] J.Lin,Q.Chen.Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion[J].Mechanical Systems&Signal Processing,2012,38(2):515-533.
[16] X.Chen,G.Yao,J.Cai,et al.Fractal and multifractal analysis of different hydraulic flow units based on micro-CT images[J].Journal of Natu-ral Gas Science&Engineering,2017,48(1):145-156.
[17] P.Pavón-Domínguez,A.B.Ariza-Villaverde,A.Rincón-Casado,et al.Fractal and multifractal characterization of the scaling geometry of anurban bus-transport network[J].Computers Environment&Urban Systems,2017,64(1):229-238.
[18] Lin Min,Ya Shuang-Xi,Zhao Gang,et al.Multifractal detrended fluctuation analysis of interevent time series in a modified OFC model[J].Communications in Theoretical Physics,2013,59(1):1-6.