基于改进学习策略的Kriging模型结构可靠度算法  

Structural Reliability Algorithms of Kriging Model Based on Improved Learning Strategy

作　　者：洪林雄 李华聪[1] 彭凯[1] 肖红亮 HONG Linxiong;LI Huacong;PENG Kai;XIAO Hongliang(School of Power and Energy, Northwestern Polytechnical University, Xi′an 710072, China)

机构地区：[1]西北工业大学动力与能源学院,陕西西安710072

出　　处：《西北工业大学学报》2020年第2期412-419,共8页Journal of Northwestern Polytechnical University

基　　金：国家科技重大专项(2017-V-0013-0065,2017-V-0010-0060);中央高校基本科研业务费专项资金(31020190MS707)资助。

摘　　要：针对机械产品可靠性分析过程中,极限状态函数隐式、高度非线性而导致可靠性求解困难等问题,提出一种基于Kriging模型和改进EGO主动学习策略的可靠性求解算法。对于传统EGO方法无法在极限状态面区域进行有效选点问题,提出一种改进的EGO方法,通过对样本点模型预测值做绝对值处理,基于响应值分布状态不变假设,将主动学习选点重心移到预测方差较大和极限状态面附近,避免对不必要区域的过量选点,从而减少极限状态函数值的计算或试验次数,有效提高了可靠性计算效率。通过3个算例表明:与传统主动学习方法相比,所提方法具有良好的全局和局部搜索能力,能够在较少计算极限状态函数次数条件下,估算得到精确的失效概率值。Aiming at the problems of implicit and highly nonlinear limit state function in the process of reliability analysis of mechanical products,a reliability analysis method of mechanical structures based on Kriging model and improved EGO active learning strategy is proposed.For the problem that the traditional EGO method cannot effectively select points in the limit state surface region,an improved EGO method is proposed.By dealing with the predicted values of sample point model with absolute values and assume that the distribution state of response values remains the same,the work focus of active learning selection points is moved to the vicinity,where the points are with larger prediction variance or close to the limit state surface.Three examples show that,compared with the classical active learning method,the proposed method has good global and local search ability,and can estimate the exact failure probability value under the condition of less calculation of the limit state function.

关 键 词：结构可靠性 KRIGING模型 主动学习 MONTE CARLO方法 失效概率 算法 

分 类 号：TB114.3[理学—概率论与数理统计]