Article(id=1228048670810108440, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228048667874095618, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.2024.03.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1654617600000, receivedDateStr=2022-06-08, revisedDate=1658592000000, revisedDateStr=2022-07-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1770719219161, onlineDateStr=2026-02-10, pubDate=1711555200000, pubDateStr=2024-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770719219161, onlineIssueDateStr=2026-02-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770719219161, creator=13701087609, updateTime=1770719219161, updator=13701087609, issue=Issue{id=1228048667874095618, tenantId=1146029695717560320, journalId=1225147924628267009, year='2024', volume='37', issue='3', pageStart='365', pageEnd='538', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770719218462, creator=13701087609, updateTime=1770795476854, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228368518803030940, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228048667874095618, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228368518803030941, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228048667874095618, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=522, endPage=527, ext={EN=ArticleExt(id=1228048671036600866, articleId=1228048670810108440, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Control method for multi-axis stationary non-Gaussian random vibration test, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Multi-axial stationary non-Gaussian random vibration control tests can simultaneously control the time-frequency characteristics of the specified response signals. A fast method for generating stationary non-Gaussian random vibration signals with specified power spectral density,skewness and kurtosis is proposed. The target power spectral density is designed as a filter by frequency sampling method. The non-Gaussian random signal is obtained by nonlinear transformation method and then it passes through the designed filter to obtain the desired non-Gaussian random signals. This method is computationally efficient and overcomes the shortcoming of the traditional nonlinear transformation methods. This proposed method is applied to the three-axis stationary non-Gaussian random vibration test,and the closed-loop equalization step of the three-axis non-Gaussian random vibration control is given,which can decouple the power spectrum auto spectrum,coherence coefficient,phase difference,skewness and kurtosis of the signal at the same time. A three-axis stationary non-Gaussian random vibration control test is carried out. The control results of power spectral density,skewness and kurtosis in three directions are satisfactory which meet the requirements of engineering application.

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多轴向平稳非高斯随机振动控制试验能够对指定响应信号的时、频域特征进行同时控制。提出了一种快速生成具有指定功率谱密度、斜度和峭度的平稳非高斯随机振动信号的方法。通过频率采样方法将目标功率谱密度设计成滤波器,利用非线性变换方法获取非高斯随机信号,再将此信号经过设计的滤波器以获得满足要求的非高斯随机信号。该方法简单、快速并克服了传统非线性变换方法的缺点。进一步将此方法应用于三轴向平稳非高斯随机振动试验中,给出了三轴向非高斯随机振动控制的闭环均衡步骤,此方法能够同时对信号的功率谱自谱、相干系数、相位差以及斜度和峭度进行解耦控制。进行了三轴向平稳非高斯随机振动控制试验,三个方向上加速度信号的功率谱密度、斜度和峭度控制效果均令人满意,满足工程应用要求。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
陈怀海(1965―),男,博士,教授。 E-mail:
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郑荣慧(1991—),男,博士,助理研究员。 E-mail:

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郑荣慧(1991—),男,博士,助理研究员。 E-mail:

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多轴平稳非高斯随机振动试验控制方法
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郑荣慧 1 , 李晋鹏 2 , 魏小辉 1 , 陈怀海 1
振动工程学报 | 2024,37(3): 522-527
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振动工程学报 | 2024, 37(3): 522-527
多轴平稳非高斯随机振动试验控制方法
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郑荣慧1 , 李晋鹏2, 魏小辉1, 陈怀海1
作者信息
  • 1南京航空航天大学航空学院航空航天结构力学及控制全国重点实验室, 江苏 南京 210016
  • 2航天科工防御技术研究试验中心, 北京 100854
  • 郑荣慧(1991—),男,博士,助理研究员。 E-mail:

通讯作者:

陈怀海(1965―),男,博士,教授。 E-mail:
Control method for multi-axis stationary non-Gaussian random vibration test
Rong-hui ZHENG1 , Jin-peng LI2, Xiao-hui WEI1, Huai-hai CHEN1
Affiliations
  • 1State Key Laboratory of Mechanics and Control for Aerospace Structures,College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • 2Defense Technology Research and Test Center,China Aerospace Science and Industry Co.,Ltd.,Beijing 100854,China
出版时间: 2024-03-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.03.017
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多轴向平稳非高斯随机振动控制试验能够对指定响应信号的时、频域特征进行同时控制。提出了一种快速生成具有指定功率谱密度、斜度和峭度的平稳非高斯随机振动信号的方法。通过频率采样方法将目标功率谱密度设计成滤波器,利用非线性变换方法获取非高斯随机信号,再将此信号经过设计的滤波器以获得满足要求的非高斯随机信号。该方法简单、快速并克服了传统非线性变换方法的缺点。进一步将此方法应用于三轴向平稳非高斯随机振动试验中,给出了三轴向非高斯随机振动控制的闭环均衡步骤,此方法能够同时对信号的功率谱自谱、相干系数、相位差以及斜度和峭度进行解耦控制。进行了三轴向平稳非高斯随机振动控制试验,三个方向上加速度信号的功率谱密度、斜度和峭度控制效果均令人满意,满足工程应用要求。

随机振动  /  平稳非高斯  /  多轴振动  /  斜度  /  峭度  /  功率谱密度

Multi-axial stationary non-Gaussian random vibration control tests can simultaneously control the time-frequency characteristics of the specified response signals. A fast method for generating stationary non-Gaussian random vibration signals with specified power spectral density,skewness and kurtosis is proposed. The target power spectral density is designed as a filter by frequency sampling method. The non-Gaussian random signal is obtained by nonlinear transformation method and then it passes through the designed filter to obtain the desired non-Gaussian random signals. This method is computationally efficient and overcomes the shortcoming of the traditional nonlinear transformation methods. This proposed method is applied to the three-axis stationary non-Gaussian random vibration test,and the closed-loop equalization step of the three-axis non-Gaussian random vibration control is given,which can decouple the power spectrum auto spectrum,coherence coefficient,phase difference,skewness and kurtosis of the signal at the same time. A three-axis stationary non-Gaussian random vibration control test is carried out. The control results of power spectral density,skewness and kurtosis in three directions are satisfactory which meet the requirements of engineering application.

random vibration  /  stationary non-Gaussian  /  multi-axial vibration  /  skewness  /  kurtosis  /  power spectral density
郑荣慧, 李晋鹏, 魏小辉, 陈怀海. 多轴平稳非高斯随机振动试验控制方法. 振动工程学报, 2024 , 37 (3) : 522 -527 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.03.017
Rong-hui ZHENG, Jin-peng LI, Xiao-hui WEI, Huai-hai CHEN. Control method for multi-axis stationary non-Gaussian random vibration test[J]. Journal of Vibration Engineering, 2024 , 37 (3) : 522 -527 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.03.017
随机振动环境试验是在实验室内考核装备结构完整性、功能性以及可靠性等方面的有效手段之一1-3。当前,随机振动试验主要模拟单轴向平稳高斯随机振动环境,即受控振动在单一方向上满足高斯分布。随着计算机技术的快速进步和对环境试验精度需求的日益提高,随机振动试验技术正在逐步向多点激励、多轴向、非高斯以及非平稳等方向发展4-5
传统平稳高斯随机振动试验中的激励是平稳高斯信号,在线性振动系统下响应也是高斯的。因此,该类试验只需控制信号的频域特征,即功率谱密度。对于平稳非高斯随机振动试验,不仅需要控制信号的功率谱密度,还需进一步控制信号的时域非高斯特征。目前,非高斯随机振动试验普遍采用峭度作为非高斯时域控制特征6-8。峭度与信号的二阶和四阶矩有关。多轴向平稳非高斯随机振动试验的试验难度更大,主要表现在如何同时解耦地控制多个响应信号的时域和频域特征。这里耦合不仅表现在多振动台物理系统的固有耦合,即某一点的响应表现为所有激励信号的贡献;同时由于功率谱密度矩阵中互谱密度的存在,造成了受控信号之间相关性耦合。
在进行平稳非高斯随机振动试验之前,首先需要能够模拟出满足要求的平稳非高斯随机振动信号。常见的非高斯随机信号生成方法主要有零记忆非线性变换方法9、相位调节方法10以及散粒噪声调制方法11。零记忆非线性变换方法具有操作简单且效率高的特点,但在获得时域非高斯特征的同时也在一定程度上改变了信号的功率谱密度,这就使得该方法不能同时保证信号的时频特征。Zheng等12提出了一种改进的非线性变换方法能够克服该缺点,但需要一个迭代过程。相位调节方法即调节信号的傅里叶相位,由于功率谱密度只与傅里叶幅值有关,因此该方法能够在保证信号非高斯特征的同时不改变信号的功率谱密度。但相位调节方法计算相对耗时,特别是在峭度值较大或者同时考虑信号斜度和峭度时。散粒噪声调制方法是将给定的功率谱密度构造成散粒噪声序列,通过补充幅值参数来控制输出信号的斜度和峭度。Smallwood11给出了三种幅值函数调节方法。
本文提出了一种可以快速生成指定斜度、峭度和功率谱密度的非高斯随机信号方法。首先,采用频率采样方法将目标功率谱密度设计成滤波器,利用零记忆非线性变换方法获得具有一定斜度和峭度的非高斯随机信号,再将此信号经过设计好的滤波器获得指定的斜度、峭度和幅值特征。最后,将此方法应用于三轴向平稳非高斯随机振动试验中。
本文所提的非高斯随机振动信号生成方法流程如图1所示。其基本原理是高斯随机信号在经零记忆非线性变换后,将得到的非高斯随机信号经过由参考谱密度设计的滤波器以获取相应的斜度、峭度和谱密度幅值特征。该方法无需迭代,根据公式计算一些必要参数即可。主要分为以下四个步骤:
1) 定义目标功率谱密度St、斜度Wt和峭度Kt,则控制目标表示为:
2) 利用目标谱密度设计线性相位有限长单位冲激响应滤波器,即FIR滤波器,并计算一些参数。利用频率采样方法,设计的滤波器可以表示为13
式中  P=2M+1。Lk从目标功率谱密度计算得到,表示为:
式中  df为频率分辨率。
参数可以由下式计算得到:
3) 利用非线性变换方法将任意高斯随机信号xt)变换为非高斯随机信号ut),非线性变换函数表示为:
用于非线性变换的参考斜度值Wnt和峭度值Knt计算如下:
参数ab可以采用非线性优化方法得到,表示为:
式中  WuKu分别为非高斯随机信号ut)的斜度和峭度。在这一步骤中,生成的非高斯随机信号的功率谱密度、斜度和峭度并不满足定义的目标要求。
4) 将上面步骤生成的非高斯随机信号ut)经过设计的滤波器,即可以得到满足目标要求的非高斯随机信号,表示为:
对信号进行均方根值修正,表示为:
式中  rt为目标均方根值,可以由目标功率谱密度计算得到。为信号的均方根值。yt)为最终得到的满足目标要求的非高斯随机信号。公式(9)并不改变信号的斜度和峭度。
通过以上四个步骤即可以生成满足目标参数要求的平稳非高斯随机信号。其中,步骤4)中滤波器的作用首先是使得信号满足目标功率谱密度特征;其次,非高斯信号经过滤波器后斜度和峭度会发生改变。步骤3)则是对信号斜度和峭度的补偿操作。
图2展示了利用所提方法生成非高斯随机振动信号的实例。在该实例中,设置目标谱如图2(a)中黑实线所示,目标斜度和峭度分别为0.2和6。其中,非线性变换方法的功率谱密度是直接利用公式(5)计算满足斜度和峭度要求的非高斯随机振动信号估计得到的。可以发现,直接用非线性变换方法得到的信号功率谱密度不能匹配目标值,其动态范围严重下降。生成的平稳非高斯时域信号片段如图2(b)所示。
本小节将非高斯信号生成方法应用至三轴向振动平稳非高斯随机振动试验中。控制参数为三轴向加速度信号的功率谱密度、斜度和峭度,可以表示为:
式中  SyySrr分别为参考谱和控制谱矩阵;WyWr分别为参考斜度和控制斜度矩阵;KyKr分别为参考峭度和控制峭度矩阵。
多输入驱动信号傅里叶矩阵可以表示为:
式中  Z为测试系统频响函数矩阵的逆矩阵;R为参考矩阵,表示为:
式中  L为下三角矩阵;Θ为随机相位矩阵。当生成初始驱动信号时有:
式中  Lr来自参考谱密度矩阵的Cholesky分解。
随机相位矩阵为下三角矩阵添加了傅里叶相位信息,并满足如下关系:
式中  I为单位矩阵。上标‘H’表示复共轭转置。
对参考矩阵做逆傅里叶变换,可得三维时域矩阵序列,表示为:
式中  N为一帧信号长度。对矩阵r求行和则可以得到三个方向上的时域参考信号。如果此参考信号满足如式(10)所示要求,即包括频域中的功率谱密度、时域中的斜度和峭度,那么受控响应信号的统计特征理论上应等于参考信号的统计特征。这就达到了随机振动控制的目的。
根据文献[14]可知,矩阵r中每一列都耦合了不同的随机傅里叶相位角,因此,仅可以调节矩阵中的对角元素使得信号的斜度和峭度满足参考要求。具体地,根据参考斜度和峭度的要求,当调节r11斜度和峭度时,矩阵r中第一列元素的斜度和峭度都被确定了;紧接着,调节r22确定第二列元素的斜度和峭度特征;最后,调节r33完成所有信号的调节。这里,对信号的调节采用第1节给出的方法,即根据不同通道信号的参考斜度和峭度对各通道信号依次调节,使得在调节信号斜度和峭度时功率谱密度不会被影响。
完成调节后,利用式(11)做傅里叶逆变换即可以得到满足要求的驱动信号。由于存在计算误差、测量噪声等影响,需要对受控制信号进行闭环控制,表示为对参考信号进行修正,即对驱动信号进行修正。修正算法表示为:
式中  η为收敛因子;为控制谱矩阵做Cholesky分解得到的下三角矩阵。
闭环均衡第(k+1)次得到的三个修正矩阵LWK被用于公式(11)以修正驱动信号。三轴向平稳非高斯随机振动控制流程如图3所示。
本小节给出了一个三轴向平稳非高斯随机振动控制实例。振动测试系统如图4所示。三个加速度传感器被安置在试验件底座上,分别测量三轴向上的加速度响应信号。PXIe数据采集与发送系统连接三轴向振动台系统和计算机。LabVIEW编写的振动控制软件程序用于控制PXIe系统,实现数据发送、采集以及处理等。
以三轴向振动台面的XYZ三个方向上的加速度信号作为控制目标。参考自谱密度、相干系数和相位差如图5~7中虚线所示。三个轴向的参考斜度分别为0,0.2和-0.1;参考峭度分别为5,5和6。从图5~7中可以看出,功率谱密度控制结果较好。15次闭环修正的斜度和峭度结果分别如图89所示。可以看到,三次闭环均衡后,三个轴向的斜度和峭度被稳定地控制在参考值附近。图10给出了三个方向上的控制信号片段。
本文研究了具有指定功率谱密度、斜度和峭度的平稳高斯随机振动信号的生成方法,并将其进一步应用于三轴向非高斯随机振动试验中。本文的主要研究工作总结如下:
(1)提出了一种快速生成具有指定功率谱密度、斜度和峭度的非高斯随机信号生成方法。通过非线性转换获得非高斯随机信号,利用功率谱密度设计出滤波器并与该非高斯信号进行卷积,最终获得满足目标功率谱、斜度和峭度要求的非高斯随机信号。
(2)实现了三轴向非高斯随机振动控制试验方法。设定多轴非高斯随机振动试验的参考条件为频域中的自谱密度、相干系数以及相位差和时域中的斜度和峭度。利用所提的非高斯随机信号生成方法获取参考信号,最终生成满足要求的多输入驱动信号,实现闭环均衡控制。
(3)给出了三轴向随机振动控制结果。对三轴向振动控制台面的三个正交加速度信号进行控制,使得响应功率自谱密度、互谱密度、斜度和峭度均得到了良好、稳定的控制。
  • 国家自然科学基金青年科学基金资助项目(12202187)
  • 中国博士后科学基金资助项目(2022M721610)
  • 中国博士后科学基金资助项目(2023T160313)
  • 江苏省卓越博士后计划(2022ZB210)
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2024年第37卷第3期
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doi: 10.16385/j.cnki.issn.1004-4523.2024.03.017
  • 接收时间:2022-06-08
  • 首发时间:2026-02-10
  • 出版时间:2024-03-28
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  • 收稿日期:2022-06-08
  • 修回日期:2022-07-24
基金
国家自然科学基金青年科学基金资助项目(12202187)
中国博士后科学基金资助项目(2022M721610)
中国博士后科学基金资助项目(2023T160313)
江苏省卓越博士后计划(2022ZB210)
作者信息
    1南京航空航天大学航空学院航空航天结构力学及控制全国重点实验室, 江苏 南京 210016
    2航天科工防御技术研究试验中心, 北京 100854

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陈怀海(1965―),男,博士,教授。 E-mail:
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2种不同金属材料的力学参数

Family
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Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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