柔性机构时变可靠性分析的时变随机响应面法

摘要
图/表
参考文献(19)
相关文章 (12)

全文: PDF (1099 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对柔性机构极限状态函数的时变、非线性和隐式问题，提出了柔性机构时变可靠性分析的一种新响应面法——时变随机响应面法。首先，将模态综合法与广义混沌多项式方法相结合，建立时变随机响应面，用以描述在随机参数和刚柔耦合影响下，系统响应随时间变化的规律，并得到系统响应的统计特征。与仅用混沌多项式拟合系统响应相比，提高了计算效率。其次，建立系统运动可靠性的时变可靠性模型，给出了时变可靠度的蒙特卡罗计算方法。最后，以两连杆柔性机械臂为例，对方法的有效性进行了验证，结果与蒙特卡罗法相比，具有较高的计算精度。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

关键词 ：柔性机构, 时变可靠性分析, 时变随机响应面, 广义混沌多项式, 模态综合法

Abstract：For the limit state analysis of flexible mechanisms with time varying,highly nonlinear and implicit characteristics,the time-varying stochastic response surface method for its time-varying reliability analysis was proposed.The component mode synthesis method was combined with the generalized polynomial chaos,and a time-varying stochastic response surface was established to obtain,the system responses and its statistical characteristics under the random parameters changing over the time.Comparing with the method of polynomial chaos expansion,the computational efficiency is improved.A time-varying reliability model for the mechanism motions was then established,and the Monte Carlo method specially for time-varying reliability analysis was provided.The effectiveness of the method was verified by the analysis of a two-link flexible manipulator.The results show that the method has high computational accuracy compared with the common Monte Carlo method.

Key words： flexible mechanism time-varying reliability analysis time-varying stochastic response surface generalized polynomial chaos component mode synthesis

收稿日期: 2017-08-07 出版日期: 2019-01-15

引用本文:

阚琳洁,2，张建国1,2，邱继伟1,2. 柔性机构时变可靠性分析的时变随机响应面法[J]. 振动与冲击, 2019, 38(2): 253-258.
KAN Linjie1,2 ZHANG Jianguo1,2 WANG Pidong1,2. Time-varying stochastic response surface method for the time-varying reliability analysis of flexible mechanisms. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(2): 253-258.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I2/253

[1] 韩彦彬, 白广忱, 李晓颖等. 柔性机构动态可靠性分析的新方法[J]. 计算力学学报, 2014, 31(3): 291-296.
HAN Yan-bin, BAI Guang-chen, LI Xiao-ying, et al. New method of dynamic reliability analysis in flexible mechanism[J]. Chinese Journal of Computational Mechanics, 2014, 31(3): 291-296.
[2] 马培良, 刘相秋, 李根成. 考虑刚柔耦合的机构运动可靠性研究[J]. 航空兵器, 2014, 2: 58-60.
MA Pei-liang, LIU Xiang-qiu, LI Gen-cheng. Research on the rigid-flexible coupling method for mechanism motion reliability analysis[J]. Aero Weaponry, 2014, 2: 58-60.
[3] WANG Jin-ge, ZHANG Jun-fu, Du Xiaoping. Hybrid dimension reduction for mechanism reliability analysis with random joint clearances[J]. Mechanism and Machine Theory, 2011, 46 (10): 1396-1410.
[4] HU Zhen，DU Xiao-ping. Time-dependent reliability analysis with joint upcrossing rates[J]. Structural & Multidisciplinary Optimization, 2013, 48 (5): 893-907.
[5] HU Zhen，DU Xiao-ping. First order reliability method for time-variant problems using series expansions[J]. Structural & Multidisciplinary Optimization, 2015, 51 (1): 1-21.
[6] ZHANG Chun-yi, BAI Guang-chen. Extremum response surface method of reliability analysis on two-link flexible robot manipulator[J]. Journal of Central South University, 2012, 19(1): 101-107.
[7] 于霖冲, 白广忱, 焦俊婷. 柔性机构动态强度可靠性分析理论和方法研究[J]. 机械强度, 2007, 29(6): 956-959.
YU Lin-chong, BAI Guang-chen, JlAO Jun-ting. Research on dynamical strength reliability analysis theory and methodology of flexible mechanism[J]. Journal of Mechanical Strength, 2007, 29(6): 956-959.
[8] 韩彦彬, 白广忱, 李晓颖等. 基于SVM回归柔性机构的动态可靠性研究[J]. 工程力学, 2014, 31 (12): 208-216.
HAN Yan-bin, BAI Guang-chen, LI Xiao-ying, et al. Dynamic reliability research of flexible mechanism based on support vector machine regression[J]. Engineering Mechanics, 2014, 31(12): 208-216.
[9] SHI Wen-sheng, GUO Jian-bin, ZENG Sheng-kui, et al. A mechanism reliability analysis method based on polynomial chaos expansion[C]// Reliability, Maintainability and Safety (ICRMS). 9th International Conference on Reliability. Guiyang China: IEEE, 2011. 110-115.
[10] Wiener N. The homogeneous chaos[J]. American Journal of
Mathematics, 1938, 60(4): 897-936.
[11] XIU Dong-bin, Karniadakis G.E. The Wiener-Askey polynomial chaos for stochastic differential equations[J]. Society for Industrial and Applied Mathematics, 2002, 24(2): 619-644.
[12] 汤涛, 周涛. 不确定性量化的高精度数值方法和理论[J]. 中国科学:数学, 2015, 45(7): 891-928.
TANG Tao, ZHOU Tao. Recent developments in high order numerical methods for uncertainty quantification[J]. Scientia Sinica(Mathematica), 2015, 45(7): 891-928.
[13] GUO Jian-bin, WANG Yao, ZENG Sheng-kui. Nonintrusive-Polynomial-Chaos-Based Kinematic Reliability Analysis for Mechanisms with Mixed Uncertainty[J]. Advances in Mechanical Engineering, 2014(10): 1-12.
[14] GUO Jian-bin, DU Shaohua, WANG Yao, et al. Time-Dependent Global Sensitivity Analysis for Long-Term Degeneracy Model Using Polynomial Chaos[J]. Advances in Mechanical Engineering, 2014(10): 1-16.
[15] 赵宽. 不确定多体系统动力学分析及可靠性预测[D]. 西安：西安电子科技大学，2014.
ZHAO Kuan. Dynamic analysis and prediction on reliability of multi-body system with uncertainty[D]. Xi’an: Xidian University, 2014.
[16] WU Long, Tiso P. Nonlinear model order reduction for flexible multibody dynamics: a modal derivatives approach[J]. Multibody System Dynamics, 2016, 36(4): 405-425.
[17] Betz W, Papaioannou I, Straub D. Numerical methods for the discretization of random fields by means of the Karhunen–Loeve expansion[J]. Computer Methods in Applied Mechanics & Engineering, 2014, 271(4): 109-129.
[18] Sarsri D, Azrar L, Jebbouri A, et al. Component mode synthesis and polynomial chaos expansions for stochastic frequency functions of large linear FE models[J]. Computers and Structures, 2011 , 89 (3) :346-356.
[19] Melchers R.E. Structural Reliability Analysis and Prediction[M]. New York: Wiley, 1999.