Reliability analysis method based on adaptive importance sampling
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摘要: 基于自适应重要抽样(AIS,Adaptive Importance Sampling)的可靠性分析方法,能够克服基于蒙特卡洛方法分析小概率事件时存在的效率低、精度差问题.为解决显性失效方程不存在时寻找失效点困难问题,首先利用条件递归寻找失效点,并使之尽可能在失效面附近;以该失效点为采样中心,抽取失效样本,并不断调整采样中心,使失效样本不断靠近设计点;然后利用这组失效样本估计重要抽样函数的参数,再执行自适应迭代过程,直至失效概率的误差缩小到允许误差限内.最后通过两个典型案例对方法进行应用验证,仿真结果表明在没有失效方程的情况下,能够通过仿真方法很快找到失效点,表明对于不存在显性失效方程的系统,该方法同样适用.与蒙特卡洛方法的对比结果表明该方法在仿真效率上具有较大优越性,且失效概率越小,这种优越性越明显.Abstract: Low efficiency and accuracy problem is serious when analyzing the rare event using Monte Carlo methods, a reliability analysis method was proposed based on the adaptive importance sampling (AIS). Finding the failure points is difficult when the failure equation is not explicit. Firstly a conditioned recursion algorithm was used to search the failure point which was as close as possible to failure surface. Then the failure point was selected as the new sample center, and the center point was adjusted iteratively to get a failure sample nearby the design point. The failure sample was used to predict the parameters of importance sampling density function; iteration will be performed during the process until the deviation of failure probability met the requirement. Finally, two typical cases were used to verify the method; simulation results show that the failure point can be searched quickly through simulation method without an explicit failure equation, which means it is applicable to the situation where failure equation is implicit. Comparison results with Monte Carlo methods prove that simulation efficiency is increased obviously, especially for the application condition with very small failure probability.
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Key words:
- reliability /
- Monte Carlo methods /
- simulation /
- adaptive importance sampling methods /
- recursion
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[1] 张艳,黄敏,赵宇,等.基于置信分布的系统可靠度评估蒙特卡罗方法[J].北京航空航天大学学报,2006,32(9):1023-1025 Zhang Yan,Huang Min,Zhao Yu,et al.Monte Carlo method for system reliability evaluation using reliability confidence distribution[J].Journal of Beijing University of Aeronautics and Astronautics,2006,32(9):1023-1025(in Chinese) [2] Tokdar Surya,Kass Robert E.Importance sampling:a review[J].Computational Statistics,2010,2(1):54-60 [3] 赵广燕,张建国.改进的重要度抽样法在机构可靠性中的应用[J].北京航空航天大学学报,2003,29(8):696-699 Zhao Guangyan,Zhang Jianguo.Application of an improved importance sampling method in mechanisms reliability[J].Journal of Beijing University of Aeronautics and Astronautics,2003,29(8):696-699(in Chinese) [4] Florentin,Olariu st.Monte Carlo variance reduction:Importance sampling techniques[C]//SYNASC 2009-11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.Washington,DC:IEEE Computer Saiety,2009:137-141 [5] Stadler J Scott,Sumit Roy.Adaptive importance sampling [J].IEEE Journal On Selected Areas In Communications,1993,11(3):309-316 [6] Sankaran Mahadevan,Prakash Raghothamachar.Adaptive simulation for system reliability analysis of large structures[J].Computers and Structures,2000,77:725-734 [7] Yun Bae Kim,Myeong Yong Lee,Deok Seon Roh.Nonparametric adaptive importance sampling for rare event simulation[C]//Proceedings of the 2000 Winter Simulation Conference.San Diego,CA:Society for Compter Simulation International,10-13,2000:767-772 [8] 朱敏,窦培林,李红涛.基于全周期抽样的搜索自适应Monte Carlo重要抽样法的船舶总纵强度可靠性研究[J].舰船科学技术,2008,30(1):71-75 Zhu Min,Dou Peilin,Li Hongtao.Reliability research of overall longitudinal strength of hull based on full periodic search adaptive Monte Carlo importance sampling method[J].Ship Science and Technology,2008,30(1):71-75(in Chinese) [9] 吴建成,吴剑国,吴亚舸.一种基于马尔克夫链模拟样本的自适应重要样本法[J].华东船舶工业学院学报,2003,17(3):8-12 Wu Jiancheng,Wu Jianguo,Wu Yage.An adaptive importance sampling method based on markov chain sample simulation algorithm[J].Journal of East China Shipbuilding Institute,2003,17(3):8-12(in Chinese) [10] Li Fan,Wu Teresa.An importance sampling based approach for reliability analysis[C]//Proceedings of the 3rd Annual IEEE Conference on Automation Science and Engineering.Scottsdale,AZ:IEEE,2007:956-961 [11] 程鹏,自动控制原理[M].北京:高等教育出版社,2006:79 Cheng Peng.Automatic control principle[M].Beijing:Higher Education Press,2006:79(in Chinese)
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