考虑状态模糊性的结构非概率可靠性分析  

Non-probabilistic reliability analysis with fuzzy failure and safe states

作　　者：姜峰 李华聪[1] 符江锋[1] 刘显为 Feng JIANG;Huacong LI;Jiangfeng FU;Xianwei LIU(School of Power and Energy,Northwestern Polytechnical University,Xi’an 710072,China;Norinco Group Testing and Research Institute,Huayin 714200,China)

机构地区：[1]西北工业大学动力与能源学院,西安710072 [2]中国兵器工业试验测试研究院,华阴714200

出　　处：《航空学报》2024年第20期290-310,共21页Acta Aeronautica et Astronautica Sinica

基　　金：国家科技重大专项(J2019-V-0016-0111)。

摘　　要：在实际工程中,有时很难明确判断结构所处的状态是安全的还是失效的,基于传统二元状态假设进行的非概率可靠性分析忽略了这种模糊性的存在,过于理想化。针对这一问题,以椭球模型量化不确定变量,引入模糊状态假设代替二元状态假设,开展了考虑状态模糊性的结构非概率可靠性分析研究:根据模糊状态假设,对结构所处的状态进行模糊描述,在此基础上结合无差别原则,发展了非概率模糊可靠度作为考虑状态模糊性时结构非概率可靠性的度量,同时开发出相应的Monte Carlo模拟方法对所提非概率模糊可靠度进行求解;为了克服Monte Carlo方法需要大量调用真实模型而导致的求解效率低下的问题,提出了一种基于主动学习Kriging的求解算法,从而建立了一套高效的能够考虑状态模糊性的结构非概率可靠性分析方法,通过算例和工程实例验证了所提可靠性分析方法的工程实用性。In practical engineering,it is sometimes difficult to clearly determine the output state of a structure.The non-probabilistic reliability analysis with the binary state ignores the fuzzy output state,which is too ideal.To solve this problem,we conduct the non-probabilistic reliability analysis with fuzzy failure and safe states by introducing the fuzzy state assumption,with the input uncertainties quantified by the ellipsoidal model.According to the fuzzy state assumption,the states of structures are described by fuzziness,and then combined with the principle of indifference.The non-probabilistic fuzzy reliability degree is developed as a measure of the non-probabilistic reliability of the structure,followed by the corresponding Monte Carlo simulation method for the non-probabilistic fuzzy reliability.To overcome the inefficiency associated with the Monte Carlo simulation method,a novel active learning Kriging method is proposed.Finally,an efficient non-probabilistic reliability analysis method with fuzzy states is established.Examples are used to illustrate the engineering practicality of the proposed non-probabilistic reliability analysis method.

关 键 词：椭球模型 非概率可靠性分析 模糊状态假设 Monte Carlo模拟方法 主动学习Kriging 

分 类 号：V19[航空宇航科学与技术—人机与环境工程] TB114.3[理学—概率论与数理统计]