多自由度非高斯随机振动控制

doi:10.7527/S1000-6893.2016.0253

固体力学与飞行器总体设计

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

多自由度非高斯随机振动控制

孟韩¹, 黄海¹, 黄舟²

1. 北京航空航天大学宇航学院, 北京 100083;
2. 中国工程物理研究院总体工程研究所, 绵阳 621900

收稿日期:2016-05-20 修回日期:2016-09-07 出版日期:2017-02-15 发布日期:2016-10-09
通讯作者: 黄海,E-mail:hhuang@buaa.edu.cn E-mail:hhuang@buaa.edu.cn

Multi-degree-of-freedom non-Gaussian random vibration control

MENG Han¹, HUANG Hai¹, HUANG Zhou²

1. School of Astronautics, Beihang University, Beijing 100083, China;
2. Institute of Systems Engineering, China Academy of Engineering Physics, Mianyang 621900, China

Received:2016-05-20 Revised:2016-09-07 Online:2017-02-15 Published:2016-10-09

摘要/Abstract

摘要：

在振动试验台上进行多自由度（MDOF）随机振动激励时，传统的控制方法生成的驱动信号及试验台的响应信号都是高斯信号。但真实的振动干扰信号多是超高斯的；而相比于高斯激励，亚高斯激励可降低驱动信号的最大幅值。为实现多自由度亚高斯和超高斯振动控制，提出一种多自由度非高斯随机振动控制方法，该方法采用系统辨识解决系统耦合问题，而后通过选择特殊的相位生成非高斯伪随机驱动信号，再经过时域随机化得到真随机非高斯驱动信号。基于Hexapod平台的多自由度微振动试验台的亚高斯和超高斯实验表明，在试验台的响应功率谱（PSD）满足工程中常用的±3 dB精度的同时，亚高斯驱动信号的最大幅值相比于高斯驱动信号的最大幅值降低了20%以上；超高斯响应信号的峭度与参考峭度的误差在0.2之内。实验结果验证了所提方法的有效性。

关键词: 非高斯随机振动控制, 多自由度, 功率谱密度, 峭度, 相位选择

Abstract:

The drive signal and the response signal generated by traditional multi-degree-of-freedom (MDOF) random vibration control method are both Gaussian signal. However, the real vibration interference signal is always super-Gaussian, while sub-Gaussian random excitation is mainly used to reduce the maximum amplitude of the drive signal. To achieve MDOF sub-Gaussian and super-Gaussian vibration control, an MDOF non-Gaussian random vibration control method is proposed, which solve the coupling problem through system identification, and select special phase to generate non-Gaussian pseudo-random drive signal, and then the pseudo-random drive signal is transformed to real random non-Gaussian drive signal through time domain randomization. The sub-Gaussian and super-Gaussian experiments based on a Hexapod-based MDOF micro vibration test bed show that the response power spectral density (PSD) of response signals obtained by the proposed method are limited to ±3 dB error band of reference PSD. Compared to that in the Gaussian experiment, the drive signal in the sub-Gaussian experiment decreases by more than 20%. In the super-Gaussian experiment, the error between the kurtosis of response signal and the reference value is within 0.2. Effectiveness of the proposed method can be validated by the experiment results.

Key words: non-Gaussian random vibration control, multi-degree-of-freedom, power spectrum density, kurtosis, phase selection

中图分类号:

V416.5
TB533

孟韩, 黄海, 黄舟. 多自由度非高斯随机振动控制[J]. 航空学报, 2017, 38(2): 220458-220465.

MENG Han, HUANG Hai, HUANG Zhou. Multi-degree-of-freedom non-Gaussian random vibration control[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(2): 220458-220465.

参考文献

[1] 陈章位, 于慧君. 振动控制技术现状与进展[J]. 振动与冲击, 2009, 28(3):73-77. CHEN Z W, YU H J. Existing state and development of vibration control technology[J]. Journal of Vibration and Shock, 2009, 28(3):73-77(in Chinese).
[2] CONNON I W. Comments on kurtosis of military vehicle vibration data[J]. Journal of the IES, 1991, 34(6):38-41.
[3] 李锦华, 李春祥, 申建红. 非高斯脉动风压的模拟研究[J]. 振动与冲击, 2009, 28(9):5-8. LI J H, LI C X, SHEN J H. Simulation of non-Guassian fluctuating wind pressure[J]. Journal of Vibration and Shock, 2009, 28(9):5-8(in Chinese).
[4] XU F, LI C R, JIANG T M. On the shaker simulation of wind-induced non-Gaussian random vibration[J]. Shock and Vibration, 2016, 2016(6):1-10.
[5] 蒋培, 张春华, 陈循, 等. 超高斯随机振动环境的疲劳强化机理[J]. 国防科技大学学报, 2004, 28(3):99-102. JIANG P, ZHANG C H, CHEN X, et al. Fatigue enhancement mechanism of the super-Gaussian random vibration environment[J]. Journal of National University of Defense Technology, 2004, 28(3):99-102(in Chinese).
[6] VAN BAREN J, VAN BAREN P, JENISON M I. The third dimension of random vibration control:2007.01.2270[R]. Warrendale, PA:SAE International, 2007.
[7] STEINWOLF A. Shaker random testing with low kurtosis:Review of the methods and application for sigma limiting[J]. Shock and Vibration, 2010, 17(3):219-231.
[8] WINTERSTEIN S R. Nonlinear vibration models for extremes and fatigue[J]. Journal of Engineering Mechanics, 1988, 114(10):1772-1790.
[9] SMALLWOOD D O. Generation of stationary non-Gaussian time histories with a specified cross-spectral density[J]. Shock and Vibration, 1997, 4(5-6):361-377.
[10] SMALLWOOD D O. Generating non-Gaussian vibration for testing purposes[J]. Sound and Vibration, 2005, 39(10):18-23.
[11] HSUEH K D, HAMERNIK R P. A generalized approach to random noise synthesis:Theory and computer simulation[J]. The Journal of the Acoustical Society of America, 1990, 87(3):1207-1217.
[12] STEINWOLF A. Approximation and simulation of probability distributions with a variable kurtosis value[J]. Computational Statistics & Data Analysis, 1996, 21(2):163-180.
[13] STEINWOLF A. Shaker random testing with low kurtosis:Review of the methods and application for sigma limiting[J]. Shock and Vibration, 2010, 17(3):219-231.
[14] STEINWOLF A. Vibration testing by non-Gaussian random excitations with specified kurtosis. Part II:Numerical and experimental results[J]. Journal of Testing and Evaluation, 2014, 42(3):672-686.
[15] 蒋瑜, 陶俊勇, 王得志, 等. 一种新的非高斯随机振动数值模拟方法[J]. 振动与冲击, 2012, 31(19):169-173. JIANG Y, TAO J Y, WANG D Z, et al. A novel approach for the numerical simulation of non-Gaussian random vibration[J]. Journal of Vibration and Shock, 2012, 31(19):169-173(in Chinese).
[16] 陈家焱, 陈章位, 周建川, 等. 基于泊松过程的超高斯随机振动试验控制技术研究[J]. 振动与冲击, 2012, 31(6):19-22. CHEN J Y, CHEN Z W, ZHOU J C, et al. Super-Gaussian random vibration test control technique based on Poisson process[J]. Journal of Vibration and Shock, 2012, 31(6):19-22(in Chinese).
[17] 陈怀海, 王鹏宇, 孙建勇. 基于逆系统方法的多输入多输出非高斯驱动信号生成[J]. 航空学报, 2016, 37(5):1544-1551. CHEN H H, WANG P Y, SUN J Y. Generating multi-input multi-output non-Gaussian driving signal based on inverse system method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(5):1544-1551(in Chinese).
[18] Department of Defense. Test method standard for environmental engineering considerations and laboratory test:MIL-STD-810G (w/CHANGE-1)[S]. Washington, D.C.:Department of Defense, 2014.
[19] SMALLWOOD D O. Multiple shaker random vibration control-An update[R]. Albuquerque, NM:Sandia National Labs, 1999.
[20] 蒋瑜, 陈循, 陶俊勇. 基于时域随机化的超高斯真随机驱动信号生成技术研究[J]. 振动工程学报, 2005, 18(4):491-494. JIANG Y, CHEN X, TAO J Y. Study on the generation of super-Gaussian and true-random drive signals using time domain randomization[J]. Journal of Vibration Engineering, 2005, 18(4):491-494(in Chinese).
[21] 黄海, 王海强, 李伟鹏, 等. 一种六自由度振动激励系统:CN104865034A[P]. 2015-08-26. HUANG H, WANG H Q, LI W P, et al. A six degree of freedom vibration excitation system:CN104865034A[P]. 2015-08-26(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

多自由度非高斯随机振动控制

Multi-degree-of-freedom non-Gaussian random vibration control

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	马益, 贺旭东, 陈怀海, 郑荣慧. MIMO窄带加宽带非高斯随机振动试验[J]. 航空学报, 2023, 44(8): 227475-227475.
[2]	张忠强, 张新, 王家序, 刘治汶. 基于重加权谱峭度方法的航空发动机故障诊断[J]. 航空学报, 2022, 43(9): 625445-625445.
[3]	瞿绍奇, 孙英超, 邬亨贵, 李延平, 张伟. 飞行器径向连接螺栓振动断裂分析[J]. 航空学报, 2021, 42(5): 524431-524431.
[4]	张洁, 庞丽萍, 完颜笑如, 陈浩, 王鑫, 梁晋. 基于脑电功率谱密度的作业人员脑力负荷评估方法[J]. 航空学报, 2020, 41(10): 123618-123618.
[5]	陈怀海, 王鹏宇, 孙建勇. 基于逆系统方法的多输入多输出非高斯驱动信号生成[J]. 航空学报, 2016, 37(5): 1544-1551.
[6]	李继超, 童志庭, 聂超群, 林峰. 基于互相关分析的前失速先兆检测分析[J]. 航空学报, 2013, 34(1): 28-36.
[7]	李金涛, 房建成. 高精度光纤IMU的磁屏蔽方法及实验研究[J]. 航空学报, 2011, 32(11): 2106-2116.
[8]	吕奇峰;张卫红;张桥;朱继宏. 随机振动响应下的组件结构布局优化设计[J]. 航空学报, 2010, 31(9): 1769-1775.
[9]	王明珠;姚卫星. 限带白噪声过程雨流幅值分布[J]. 航空学报, 2009, 30(6): 1007-1011.
[10]	谢文忠;郭荣伟. 一种平面埋入式进气道气动特性的试验[J]. 航空学报, 2008, 30(6): 1460-1466.
[11]	丰志强;阎楚良;张书明. 飞机机载设备振动环境谱的数据处理与编制[J]. 航空学报, 2006, 27(2): 289-293.
[12]	孟光;夏南. 多自由度强非线性柔性转子—挤压油膜阻尼器系统的分叉与混沌响应[J]. 航空学报, 2003, 24(1): 42-45.
[13]	申凡;郑敏;陈怀海;鲍明. 采用频域多参考点模态识别法进行工作状态下的模态识别[J]. 航空学报, 2002, 23(4): 294-297.
[14]	张义民;闻邦椿. 具有独立失效模式的多自由度非线性振动系统的可靠性分析[J]. 航空学报, 2002, 23(3): 252-254.
[15]	周力;王琪;顾平. π-TFSK时频调制信号的功率谱分析[J]. 航空学报, 2002, 23(2): 187-189.