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基于MF-DFA特征和LS-SVM算法的刀具磨损状态识别 关山,庞弘阳,宋伟杰,康振兴

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    基于MF-DFA特征和LS-SVM算法的刀具磨损状态识别
    关 山,庞弘阳,宋伟杰,康振兴
    (东北电力大学机械工程学院,吉林 132012)
    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的独特优势,该文提出了基于多重分形去趋势波动分析和最小二乘支持向量机的刀具磨损状态识别方法。首先,用MF-DFA(multifractal detrended fluctuations analysis)方法处理去噪后的刀具磨损声发射信号,讨论其长程相关性和分形特性;然后,分析对比了不同磨损阶段下多重分形谱参数的变化,筛选出能灵敏表征刀具磨损状态的多重分形谱参数:分形维数最大值点对应的奇异指数α0,多重分形谱谱宽Dα和广义Hurst指数波动均值

                                   
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    作为特征量;最后,利用LS-SVM(least square support vector machine)算法并对比支持向量机和神经网络算法实现刀具磨损状态识别,结果表明LS-SVM算法识别率最高,平均识别准确率达97.78%,验证了本文所提方法的有效性。试验结果表明,提取的特征对刀具磨损状态的变化非常敏感,可以分离相近的磨损状态,为刀具状态监测提供一种参考方法。
    关键词:切削刀具;刀具磨损;声发射;状态识别;多重分形;去趋势波动分析;支持向量机
    0 引 言

    切削是机械加工中的重要工序,为提高机械加工的自动化和智能化水平,提高生产效率和质量,迫切要求对刀具磨损状态进行可靠监测,磨损状态特征提取是实现刀具磨损状态监测的关键[1]。近年来学者运用时频谱、功率谱、小波变换等手段对切削力信号、振动信号和声发射信号等对刀具磨损状态进行监测[2-4]。刀具磨损过程中发出的声发射(acoustic emission,AE)信号受到刀具磨损,材料晶格滑移,刀具与工件摩擦以及刀具破损影响,呈现出随机性、非线性和耗散性的特点,传统线性信号处理方法难以精确提取磨损阶段特征[5]。

    笔者近几年的研究中运用非线性手段分析了刀具磨损AE信号的混沌特性和云特性,提高了识别准确率[6-7],然而这些特性不能表征刀具磨损的内在动力学特性。分形理论描述了自然界大量存在的偶然性和不规则,近年来随着研究的深入,在故障诊断领域得到了一定的应 用[8-13]。文献[9-13]以振动信号、AE信号以及刀口形貌图像等为研究对象,利用广义分形维数作为特征量,实现了对机械设备故障特征的提取,在整体反映非线性信号的分形特性上取得了一定效果,然而仅采用单分形方法很难准确反映刀具磨损过程中复杂的内在动力学特性。Kantelhardt等[14]在单分形的基础上,提出了多重分形去趋势波动分析(multifractal detrended fluctuations analysis,MF-DFA)方法,既可以反映非线性信号的整体分形特性,也具有较强的局部分析能力,能够准确描述信号的局部动特性。目前MF-DFA方法在信号处理领域取得了一定的进展[15-17]。文献[15]应用MF-DFA方法于液化泵退化特征提取中,分析了分形谱参数对液化泵不同退化状态的反映能力。文献[16]利用MF-DFA方法估计分形谱参数作为齿轮箱故障特征量,为齿轮箱故障特征提取提供了一种新方法。文献[17]采用MF-DFA方法分析了风电场风速时间序列波动,实现了风速变化趋势的预测。

    针对刀具磨损AE信号通常具有随机性强和易埋没于噪声的特点,笔者提出一种基于MF-DFA和最小二乘支持向量机(least square support vector machine, LS-SVM)算法的刀具磨损状态特征识别方法。首先,用MF-DFA方法处理去噪后的刀具磨损AE信号,讨论其长程相关性和分形特性;然后,分析对比了不同磨损阶段下多重分形谱参数的变化,筛选出能灵敏表征刀具磨损状态的多重分形谱参数作为特征量;最后,利用LS-SVM算法实现不同刀具与材料组合切削的刀具磨损状态识别,验证本文所提方法的有效性,以期提高磨损监测准确性。

    1 基于MF-DFA的多重分形谱分析1.1 MF-DFA理论简介

    对于长度为N的非平稳时间序列 277b07d432a325f9185eea7eded2bac0.jpg ,MF-DFA过程如下[18]:

    1)计算序列xi偏离均值的累计离差y(i)

    f88c261106f7991f0cc34fe24b31f009.jpg (1)

    式中xi为原始序列,


                                   
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    为原始序列的均值。

    2)将y(i)划分为互不重叠的长度(尺度)为s的Ns(Ns= int(N/s))个等长子序列。为保证序列信息不丢失,则再从y(i)尾端向前划分一次共得到2Ns个子序列。

    3)利用最小二乘法拟合等长子序列的局部趋势函数yv(i)

    a1ae02b190a6eeff28fffb9b9a99a352.jpg (2)

    式中ai为拟合多项式的系数,i=0, 1,…, k,k为多项式拟合最高阶数。

    4)计算均方误差函数F2(v,s)

    ef58c76c34e6eaa7d9d5b2a9b05921cc.jpg (3)

    5)确定对于2Ns个子序列全序列的q阶波动函数Fq(s)

    f75625f633b5abc840500a601529ae4f.jpg (4)

    式中阶数q的取值范围为非零实数,当q=2时则为经典的DFA法,即表示尺度s下波动的均方误差F2(s)。此外,当q<0时,Fq(s)依赖于F2(v,s)的小波动,当q>0时,Fq(s)依赖于F2(v,s)的大波动。

    当q=0时,波动函数由式(5)确定。

    681c2f15a3a41c7844dc61b3e85f2f85.jpg (5)

    6)q对Fq(s)的标度行为和长程相关特性的描述体现于h(q)上,若q值变化,h(q)不是唯一值,则原始序列是多重分形过程,否则原始序列是一个单分形过程;若原始序列{xi}具有相关性,则F2(s)与s成幂律关系,即:

    5b3cce5ed065d8a76fcd911c40d84dbd.jpg (6)

    h(q)被称为广义Hurst指数,表征原始序列相关性,可用最小二乘法线性拟合log(F2(s))与log(s)得到的双对数曲线斜率表示。当H=h(2)时描述的是序列的长程相关性,被称为长程相关指数,对于平稳时间序列h(2)就是Hurst指数H。当0.5<H≤1,说明序列是持久的长程相关性,即将来会延续过去的递增、递减趋势的性质;H<0.5,表明序列是负的、反持久的长程相关性,即将来与过去递增、递减趋势相反;当H=0.5,意味着该序列是一独立随机过程,不相关。

    1.2 MF-DFA和经典多重分形理论的关系

    通过MF-DFA方法得到的h(q)和经典多重分形理论中由标准配分函数得到的τ(q)存在式(7)关系[19]:

    c6374d742d9d6ead7ddd2973e7e41f50.jpg (7)

    结合Legendre变换[20]对式(7)等号两边对q求导得到多重分形谱f(α),奇异指数α和τ(q)三者之间的关系为

    9892ca51422ac785bdca8dec1107b9b2.jpg (8)
    1.3 估计多重分形谱特征参数

    由多重分形谱可得到多重分形的3个重要参数:Δα,α0和Df。多重分形谱宽度Δα=αmax-αmin,反映信号多重分形特性的强弱,多重分形特征越强,Dα越大。极值点对应的奇异指数α0(fmax=f(α0)),反映信号的随机性,随机性越大,α0越大。多重分形谱维度Df = f(αmax) -f(αmin),反映信号最大、最小峰值出现频率的变化,Df小于0,表明概率最大子集数目大于概率最小子集数目;反之亦然。

    广义Hurst指数h(q)反映了信号不同尺度之间的关系,是衡量信号多重分形特性的特征量。在以q为横坐标、h(q)为纵坐标的坐标系上,若h(q)与q呈近似平滑直线,则信号没有多重分形特性;若h(q)与q呈非线性递减曲线,则信号具有多重分形特性。且h(q)的波动越大,信号的多重分形特性越强。因此,把广义Hurst指数的波动均值


                                   
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    作为刀具磨损特征量之一对磨损状态进行分析,如式(9)所示。

    99e488f780908834eb24033bb84622e6.jpg (9)

    式中h(0)为h(q)中值。

    由以上分析可知,多重分形谱参数能够定量反映信号的内在波动及其剧烈程度,因此笔者选用Dα,α0,Df和


                                   
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    4个特征参数作为刀具磨损状态识别特征参数进行研究。

    2 刀具磨损状态识别策略

    支持向量机(support vector machine,SVM)结构简单,泛化能力较好,近几年得到了广泛的研究[21]。当训练集规模很大时,求解标准支持向量机容易出现算法复杂、效率低等问题。因此,文献[22]提出了一种最小二乘支持向量机LS-SVM改变了标准SVM的风险函数和约束问题,用求解线性方程组替代二次规划问题,大大降低了计算的复杂度[23-25]。

    支持向量机中的正则化参数和核函数参数对模型的分类性能有很大影响,优化过程中参数之间相互影响,不能使结果最优[26]。本文运用Simplex迭代算法[27]进行参数优化,并结合舍一交叉验证构建最优模型对每组参数组合的性能进行综合判断,来确定正则化参数和核函数参数。

    给定样本数为n的数据集 5eefe34d0ed2084d18af7b01a810606f.jpg ,其中Xi为第i个输入样本,Yi为其对应的类别标识,分类的目标是确定决策函数Y(X)=sign(f(X)),函数f(X)具有如下的形式:

    b549829ed5d87601f84df108060471dd.jpg (10)

    式中


                                   
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    为输入空间到高维特征空间的映射,偏置量b和权值矢量w为待求量。未知量可通过如下最优化问题来确定:

    9a9d0436ea619eb64ead89b18bf6a826.jpg (11)

    式中γ为正则化参数,


                                   
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    &#206;R(i=1,…, n)为松弛变量。

    约束条件为:

    fac9639651c4c238ac7744c4c1b27114.jpg (12)

    利用Lagrange法解式(11)得到:

    0d9e70ab78a1635e44c84ef925c22af7.jpg (13)

    式中a¢=[a¢1,…, a¢n]T为Lagerange乘子,Y=[Y1,…, Yn]T,I为n阶单位矩阵,


                                   
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    为核函数。最终得到函数估计的LS-SVM模型为:

    823a957fc4e6ecf03075887cc427bbfe.jpg (14)

    本文中LS-SVM的输入为特征向量数据,输出为离散数值对应刀具3个磨损阶段。

    3 试验与结果分析3.1 试验数据来源及分析

    为检验方法的实用性和有效性,将提出的方法用于刀具磨损状态识别。实测刀具磨损AE信号来源于刀具磨损试验系统,如图1a所示。刀具磨损切削试验在CA6140车床进行,声发射传感器依靠磁力紧紧吸附在刀柄近刀头而不干扰切削的位置。本试验将刀具作为研究对象,一方面通过R15-ALPHA谐振式声发射传感器、PXPAⅡ宽带前置声发射放大器、PXI-6366数据采集卡(采样频率为2 MHz)、计算机等构建了数据采集系统采集AE信号;另一方面使用显微镜测量刀具磨损量的大小(精度为0.01 mm),建立刀具磨损状态信号和磨损量的对应关系。

    0b99224298d2f93ee7bd4d66553b8122.jpg
    [size=0.8em]图1 刀具YT15切削高温合金GH4169信号的采集
    [size=0.8em]Fig.1 Signal acquisition for cutting superalloy material GH4169 by cutting tool of YT15

    试验中采用2两种刀片:YT15硬质合金涂层刀片、KC9125硬质合金涂层刀片;2种试验车削材料为:退火态高碳钢T10、高温合金GH4169。将刀片与试验材料交叉共产生4种组合,切削材料确定后,由于刀具寿命主要由切削三要素决定,因此每种组合考虑切削速度、进给量、切削深度三要素,以刀具磨损量为指标,设计三因素三水平正交试验以确定刀具不同磨损损状态对应的信号采集时间。由于高温合金GH4169对刀具的磨损速率较快,刀具达到进一步磨损的时间较短,因此更换新刀后,在上一次切削时间上延迟切削20 s进行一次数据采集;而退火态高碳钢T10对刀具的磨损速率较低,在上一次切削时间上延迟切削180 s进行一次数据采集,在数据采样完成后同时进行刀具磨损量的测量,以YT15刀具切削退火态高碳钢T10为例制定了图1b所示的信号具体采集过程和表1所示的刀具3种磨损状态界定范围和磨损极限。为了使信号更好地反映刀具当前的磨损状态,所以仅记录每次切削过程最后5 s的数据,减少数据采集量;更换新刀片的目的在于更精准地模拟刀具连续切削的过程。

    [size=0.8em]表1 切削退火态高碳钢T10时刀具磨损阶段定义
    [size=0.8em]Table 1 Definition range of tool wear stage and wear limit with cutting materials of annealed high-carbon steel T10
    b8349c0c0d3d3b1920a2eb5032d67d96.jpg

    本文以切削速度为 520 r/min,切削深度为 0.5 mm,进给量为0.176 mm/r时采集的AE信号为例进行说明。研究选取初期、正常、急剧磨损状态下各60组样本,每个样本取8 192个采样点。图2为YT15硬质合金刀具与T10组合切削在不同磨损阶段10 000个采样点的AE信号时域波形。由图2可见,不同磨损阶段的AE信号在时域结构上波动复杂且具有较明显的差异,磨损状态信号隐藏于背景噪声中,如果直接采用此信号来分析,则难以提取正确的磨损阶段特征。将采集的AE信号先用小波包分析进行去噪处理,基于最小Shannon准则来确定小波包分解最佳树并重构[28-30],来达到信号初步去噪的目的。

    3.2 磨损AE信号的长程相关性和分形特性

    利用MF-DFA方法分析刀具磨损AE信号的多重分形特性,要求波动函数Fq(s)与子序列长度s有良好的对数线性关系[31-32]。当s取40,多项式拟合阶数k取1~6,q=[-10, -9, …, 9, 10]时,讨论q=2时,yv(i)中k与H的关系。采用最小二乘法线性拟合Hurst指数,采用式(15)计算拟合决定系数R2,R2的数值都大于0.9,表明拟合直线满足统计检验。

    2f28a8de1432ca48f61fa02687612533.jpg (15)

    [size=1em]式中


                                   
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    为序列

                                   
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    的均值;

                                   
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    为q阶拟合值;

                                   
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    为拟合序列

                                   
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    的均值,N为序列长度。

    [size=0.8em] 3072a9d92734d77d7e5c65374f62be73.jpg
    [size=0.8em]图2 不同磨损状态AE信号的时域波形
    [size=0.8em]Fig.2 AE signal waveform under different wear conditions

    图3给出了YT15硬质合金刀具与T10组合切削在正常磨损阶段k值变化下Hurst曲线拟合结果。图3a为磨损AE信号均方误差函数Fq(s)与尺度s随阶数k的变化的双对数关系。每条均方误差函数Fq(s)与尺度s的双对数曲线线性拟合的直线斜率就是Hurst指数H,结合表2中H的数值分析:Hurst指数H为根据实际的log Fq(s)与logs值用最小二乘线性拟合方法拟合的曲线斜率,决定系数R2表示Hurst指数H拟合值与实际点的决定系数,计算方法如式(15);当k取不同值时,logFq(s)与logs都具有较良好的线性关系;H随k的增大呈现波动性,但都大于0.5小于1(0.5<H<1),说明刀具磨损时间序列的是具有长程相关性的有序过程,内部波动不随机,具有维持趋势发展的能力。

    afb0d3c398eedb3f389ee3b11bfb321a.jpg
    [size=0.8em]图3 拟合阶数k值变化下Hurst指数拟合结果
    [size=0.8em]Fig.3 Hurst exponent fitting results with changes of fitting order k

    当k取不同值时,磨损AE信号的广义Hurst指数h(q)与波动阶数q关系曲线变化如图3b所示。h(q)随q的增大而减小,呈非线性递减关系,表明磨损AE信号存在不规则多重分形特征,具有不同的内在动力学特性。当q=2时,h(q)等于经典Hurst指数H[33-34],对于任意k,H的值均大于0.5,说明磨损AE信号具有长程相关特性。

    [size=0.8em]表2 Hurst指数的拟合结果
    [size=0.8em]Table 2 Fitting result of Hurst exponent
    d9dad4c46e034bfb27ba281f5699f0fd.jpg

    为描述刀具不同磨损阶段AE信号不同层次的波动,讨论k=1时Fq(s)与s的幂律关系曲线如图4所示,可以看出logFq(s)与logs呈良好的线性关系,即Fq(s)与s存在幂律关系,即不同磨损阶段下的AE信号在一定尺度上存在标度不变性,具有多重分形特征。

    d9a70c5b3c7a39a3ee2aa9244a746dd4.jpg
    [size=0.8em]图4 不同磨损阶段下均方误差函数Fq(s)与尺度s的幂律关系
    [size=0.8em]Fig.4 Relation between the mean square error function Fq(s) and scale s under different wear stages
    3.3 多重分形谱特征参数

    利用MF-DFA方法分别计算刀具3个磨损阶段的AE信号的多重分形谱参数的平均值,如表3所示。可知参数α0和Dα随磨损阶段的推进而递增,表明磨损量越大,AE信号的波动程度越大,整个分形结构上概率测度越不均匀,波动越随机;不同磨损状态下AE信号Df的值均小于0,多重分形谱为左钩状,表明概率测度最大子集的数量较多,且正常磨损阶段的绝对值最小,说明此阶段波动剧烈程度最小;参数 df44cc3fe531d454612fafdb705a3824.jpg 的值随磨损量的增大而递增,说明h(q)波动程度随磨损量的增大而递增,多重分形特性越强烈。

    [size=0.8em]表3 不同磨损阶段的多重分形谱参数平均值
    [size=0.8em]Table 3 Mean value of multi-fractal spectrum parameters at different wear stages
    ef64faee18df387d5195f577fe86268a.jpg

    为进一步分析不同分形谱参数反映刀具磨损状态的敏感度,图5给出了3种磨损阶段特征参数α0,Dα,Df和


                                   
    登录/注册后可看大图
    的分布。参数α0能准确地区分不同磨损状态;对于参数Dα和

                                   
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    ,除Dα中初期磨损阶段和正常磨损阶段和

                                   
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    中正常磨损阶段和急剧磨损阶段存在少许交叉外,其余部分基本可以清晰区分;参数Df的分布存在严重的混叠现象且波动较大,难以区分不同的磨损阶段。由上述分析,本文选择极点对应的奇异指数α0,多重分形谱谱宽Dα和广义Hurst指数波动均值

                                   
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    3个参数作为磨损特征量。

    f6bdd56d673c54b2fa44c6e23486d949.jpg
    [size=0.8em]图5 不同磨损阶段AE信号特征分布
    [size=0.8em]Fig.5 Characteristic distribution of AE signals at different wear stages

    综上分析,利用MF-DFA方法计算刀具磨损AE信号的多重分形谱参数,构造表征刀具磨损阶段的三维特征向量M。

    be2c1b9cfff8045c895e59ae261417b8.jpg (16)

    计算不同磨损阶段的特征向量,绘制三维分布散度图如图6所示,可以看出:采用文中提出的方法提取的刀具磨损状态特征能很好地表征刀具的磨损状态。

    6db9e7e55bde090c25120c65c6ac6dcb.jpg
    [size=0.8em]注:vc表示切削速度,f表示进给量,ap表示切削深度。
    [size=0.8em]Note:vc represents cutting speed, f represents feeding rate, and ap represents cutting depth.
    [size=0.8em]图6 不同刀具与切削材料组合下不同磨损阶段特征参数的三维散度图
    [size=0.8em]Fig.6 Three dimensional divergence diagram of characteristic parameters in different wear stages under different cutting tools and cutting materials
    3.4 刀具磨损状态识别

    将YT15硬质合金刀具与T10组合下信号提取的不同磨损阶段去噪后信号的多重分形特征参数作为输入,其中90组训练样本,90组识别样本,每种磨损阶段均各30组。采用SVM和LS-SVM进行分类,核函数类型均为RBF。运用交叉验证方法[35]优化SVM分类器参数结果正则化参数σ2为4.128 7,核函数参数γ为12.351 5,优化LS-SVM得到的σ2为0.194 1,γ为3.712 3。

    为验证基于LS-SVM方法识别分类器的有效性,另外采用相同的训练样本及测试样本特征量输入L-M(Levenberg-Marquardt)优化算法BP[36-37]神经网络中进行识别比较。神经网络中隐层节点数的选择至关重要[38],本文根据隐层节点数选取规则计算不同节点数下BP识别结果如表4所示,综合表中收敛次数、训练时间和识别率选择3-6-3 BP神经网络进行识别。

    分别计算测试样本中成功识别的样本占总测试样本的百分比为识别准确率,表5为3种分类器的识别率比较,可以看出,采用LS-SVM方法的识别率高于传统SVM的识别率并明显高于BP神经网络的识别率。

    [size=0.8em]表4 不同隐层节点数的BP模型对磨损状态识别结果
    [size=0.8em]Table 4 Recognition results of wear state with BP model with different number of nodes
    596c4b74ed8ff7389195770a7d16d6d7.jpg

    [size=0.8em]表5 三种分类器识别率对比
    [size=0.8em]Table 5 Comparison of recognition rate among three classifiers
    f48a64cd86108ad53fc5fdc363ecc5c3.jpg

    4 结 论

    1)利用MF-DFA(multifractal detrended fluctuations analysis)方法对刀具不同磨损阶段下的AE信号进行分析,结果表明刀具磨损AE信号具有长程相关性和明显的多重分形特性,且多重分形谱参数:极值对应奇异指数α0,多重分形谱谱宽Dα,广义Hurst指数的波动均值 df44cc3fe531d454612fafdb705a3824.jpg 能作为够敏感表征刀具磨损阶段特征,刀具不同磨损阶段可以被清晰区分。

    2)通过刀具磨损实测AE信号的研究,结果表明基于MF-DFA(multifractal detrended fluctuations analysis)和LS-SVM(least square support vector machine)的方法提取的多重分形谱特征能够很好地识别出刀具不同磨损阶段,验证了该识别方法的有效性,对比支持向量机和神经网络识别结果,LS-SVM算法识别率最高,平均准确率可达97.78%,为实现磨损量预测打下基础。

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    Cutting tool wear recognition based on MF-DFA feature and LS-SVM algorithm
    Guan Shan, Pang Hongyang, Song Weijie, Kang Zhenxing
    (School of Mechanical Engineering, Northeast Electric Power University, Jilin 132012, China)
    Abstract: Cutting is an important process in machining. In order to improve the automatic and intelligent level of machining and improve the production efficiency and quality, it is urgent to monitor the tool wear state. The feature extraction of wear state is the key to the tool wear monitoring. In view of the unique advantages of multifractal theory in accurately depicting the nonlinear phenomena and processes of the system, a tool wear state recognition method based on multifractal detrended fluctuation analysis (MF-DFA) and least squares support vector machine (LS-SVM) is proposed. The acoustic emission (AE) signal is denoised with wavelet packet analysis, and the best tree of wavelet packet decomposition is determined and reconstruction is performed based on the minimum Shannon criterion so as to achieve the purpose of signal initial denoising. Firstly, the MF-DFA method is used to deal with the noise emission signals of the tool wear after denoising, and the long range correlation and fractal characteristics are discussed. It shows that the tool wear time sequence is an orderly process with long range correlation, and the internal fluctuation is not random, and it has the ability to maintain the trend. Then, the multifractal spectrum parameters of different wear stages were analyzed and compared. The parameters of singular exponent corresponding to the point of extreme value and multifractal spectrum widthare increasing with the progression of the wear stage, which indicates that the greater the wear amount, the greater the fluctuation of the AE signal, the more uneven the probability measurement of the whole fractal structure, the more random the fluctuation. The values of the AE signal multifractal dimensionunder different wear states are less than zero, and the multifractal spectrum is left hook like, indicating the number of the maximum subset in the probability measure is relatively large. The absolute value of the normal wear stage is the smallest, which indicates that the volatility is the smallest in this stage; the value of the parameter increases with the increase of the wear amount, indicating that the greater the fluctuation degree of generalized Hurst exponent, the stronger the multifractal characteristics. The singular exponent corresponding to the point of extreme value, the multifractal spectrum widthand the mean of the generalized Hurst exponent, which can sensitively characterize the tool wear state, were selected as the characteristic quantities, and the three-dimensional feature vectors were constructed to characterize the tool wear stage. The clustering effect of the extracted tool wear state characteristics was obvious. The LS-SVM algorithm, SVM algorithm and BP (back propagation) neural network are applied to recognize the tool wear state. Simplex iterative algorithm is used to optimize the parameters, the optimal model is constructed to determine the performance of each group of parameters, and the parameters of regularization and kernel function are determined. The average recognition accuracy is 97.78%. The results show that the tool wear AE signal has long range correlation and obvious multifractal characteristics, the multifractal parameters, i.e. singular exponent corresponding to the point of extreme value,multifractal spectrum widthand mean of the generalized Hurst exponent can be used as sensitive characterization for the feature of tool wear stage, and the tool wear stages can be clearly distinguished. The multifractal spectrum features extracted with the method based on MF-DFA and LS-SVM can identify the different wear stages of the tool well, verify the effectiveness of the recognition method, improve the accuracy of recognition, and lay a foundation for the realization of the wear prediction.
    Keywords: cutting tools; wear of cutting tools; acoustic emission; state recognition; multifractal; detrending wave method; support vector machine
    收稿日期:2018-02-01
    修订日期:2018-05-16
    基金项目:吉林省科技厅科技公关计划(20170520099JH);吉林省省教育厅“十二五”科学技术研究项目(20150249)
    作者简介:关 山,男,吉林省吉林市人,博士,教授,主要从事机械设备故障诊断研究。Email:guanshan1970@163.com
    doi:10.11975/j.issn.1002-6819.2018.14.008
    中图分类号:TH165+.3;TP206
    文献标志码:A
    文章编号:1002-6819(2018)-14-0061-08
    关 山,庞弘阳,宋伟杰,康振兴. 基于MF-DFA特征和LS-SVM算法的刀具磨损状态识别[J]. 农业工程学报,2018,34(14):61-68. doi:10.11975/j.issn.1002-6819.2018.14.008 http://www.tcsae.org
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