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一种新的非高斯随机振动信号的模拟方法

夏静 袁宏杰 徐如远

夏静, 袁宏杰, 徐如远等 . 一种新的非高斯随机振动信号的模拟方法[J]. 北京航空航天大学学报, 2019, 45(2): 366-372. doi: 10.13700/j.bh.1001-5965.2018.0299
引用本文: 夏静, 袁宏杰, 徐如远等 . 一种新的非高斯随机振动信号的模拟方法[J]. 北京航空航天大学学报, 2019, 45(2): 366-372. doi: 10.13700/j.bh.1001-5965.2018.0299
XIA Jing, YUAN Hongjie, XU Ruyuanet al. A new simulation method of non-Gaussian random vibration signal[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 366-372. doi: 10.13700/j.bh.1001-5965.2018.0299(in Chinese)
Citation: XIA Jing, YUAN Hongjie, XU Ruyuanet al. A new simulation method of non-Gaussian random vibration signal[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 366-372. doi: 10.13700/j.bh.1001-5965.2018.0299(in Chinese)

一种新的非高斯随机振动信号的模拟方法

doi: 10.13700/j.bh.1001-5965.2018.0299
基金项目: 

国防基础科研计划 61325102

详细信息
    作者简介:

    夏静  女, 硕士研究生。主要研究方向:可靠性与环境试验技术

    袁宏杰  男, 博士, 副教授, 硕士生导师。主要研究方向:可靠性评估与验证、环境试验设计等

    通讯作者:

    袁宏杰, E-mail: yuanhongjie@buaa.edu.cn

  • 中图分类号: TP391.9

A new simulation method of non-Gaussian random vibration signal

Funds: 

National Defense Basic Scientific Research Program of China 61325102

More Information
  • 摘要:

    传统的环境振动试验常将随机振动信号假设服从高斯分布,且利用功率谱密度(PSD)来作为试验条件。然而实际环境中结构所受到的振动激励很多呈现非高斯性,且PSD只能描述信号的低阶统计量,无法描述非高斯信号的峭度和偏度等高阶统计量。针对此情况,研究了在窗函数幅值调制法(AMT)基础上利用自PSD和峭度生成非高斯信号的方法。针对调制信号的生成,提出了近似模拟方法。通过Weibull和Beta 2种分布构造调制信号,研究分布参数与目标峭度值之间的关系,并分析2种分布合成目标峭度值的范围。案例验证了仿真生成的非高斯信号与实测外场数据具有相同的PSD、概率密度函数(PDF)和峭度值,进而证明了方法的正确性。

     

  • 图 1  目标峭度与参数β的关系

    Figure 1.  Relationship between aimed kurtosis and parameter β

    图 2  参数β为[0.2,10.2]时对应调制信号的PDF

    Figure 2.  PDF of modulation signal when β is [0.2, 10.2]

    图 3  目标峭度与形状参数r的关系

    Figure 3.  Relationship between aimed kurtosis and shape parameter r

    图 4  形状参数r为[1.28,1.88]时对应调制信号的PDF

    Figure 4.  PDF of modulation signal when shape parameter r is [1.28, 1.88]

    图 5  服从Weibull分布的调制信号

    Figure 5.  Modulation signal following Weibull distribution

    图 6  实测外场数据和随时间变化的RMS

    Figure 6.  Measured ourfield data and variation of RMS with time

    图 7  实测外场数据和平滑处理数据的PSD

    Figure 7.  PSD of measured outfield data and smooth data

    图 8  实测外场数据RMS的自相关函数

    Figure 8.  Auto correlation function of RMS of measured outfield data

    图 9  仿真的高斯信号

    Figure 9.  Simulated Gaussian signal

    图 10  实测外场数据和仿真的高斯信号的PSD

    Figure 10.  PSD of measured outfield data and simulated Gaussian signal

    图 11  仿真的调制信号

    Figure 11.  Simulated modulating signal

    图 12  实测外场数据RMS和Weibull信号的自相关函数

    Figure 12.  Auto correlation function of measured outfield data RMS and Weibull signal

    图 13  仿真的非高斯信号

    Figure 13.  Simulated non-Gaussian signal

    图 14  实测外场数据和仿真非高斯信号的PSD

    Figure 14.  PSD of measured outfield data and simulated non-Gaussian signal

    图 15  不同信号的概率密度函数

    Figure 15.  PDF of different signals

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出版历程
  • 收稿日期:  2018-05-24
  • 录用日期:  2018-08-24
  • 刊出日期:  2019-02-20

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