Rare‐event component importance for the consecutive‐k system

Various indices of component importance with respect to system reliability have been proposed. The most popular one is the Birnbaum importance. In particular, a special case called uniform Birnbaum importance in which all components have the same reliability p has been widely studied for the consecutive‐k system. Since it is not easy to compare uniform Birnbaum importance, the literature has looked into the case p = ½, p → 1, or p ≥ ½. In this paper, we look into the case p → 0 to complete the spectrum of examining Birnbaum importance over the whole range of p. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 159–166, 2002; DOI 10.1002/nav.10001     

稳态随机过程激励下基于首次超越破坏的结构优化设计

把结构系统动力可靠性分析与最优化设计结合起来 ,以结构系统的最小质量为目标函数 ,给出了考虑在平稳随机过程激励下多自由度线性系统总的可靠性的结构优化设计方法。运用谱分析理论 ,推导了结构系统在平稳随机过程激励下响应的统计特征 ,同时结合首次超越破坏的Possion模型计算结构系统的可靠性 ,最终采用广义乘子法得到结构系统设计变量的最优值。计算结果表明该方法是可行的     

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

综合保障理论之海军航空装备保障发展

现役的航空装备大部分以可靠性为中心来开展设计和维修保障工作,普遍存在装备可用性低、装备预防性维修周期不合理、装备保障成本高等问题,影响了装备保障效能的提升。针对该问题,论文梳理了综合保障理论产生和发展的意义,分析了当前海军航空装备综合保障的现状,针对性提出了几点发展建议和措施,对于开展综合保障工作有积极的推动意义,对现役装备的预防性维修周期规划、可靠性增长、可用性提高、降低保障成本有重要意义。     

复杂网络可靠性研究进展

在分析网络可靠性概念和内涵的基础上,从基本可靠性和任务可靠性两个方面,以网络系统的生存性、抗毁性、可用性、可信性和完成性为主线,系统地总结了网络可靠性的相关研究进展,对未来发展趋势进行了展望。     

并联系统可靠性分配的模糊层次分析法

综合考虑影响系统可靠性分配的定性因素和定量因素、影响因素的模糊性以及系统的结构组成.利用层次分析法可集定性与定量分析于一体,从而确定出各影响因素对可靠性分配的权重向量;模糊数学理论进而确定出分系统之间可靠性水平的相对比重;进行模糊变换最终进行并联系统的可靠性分配.结合实例分析,对并联系统运用模糊理论和层次分析法相结合的方法进行可靠性分配科学、合理,便于工程应用.     

基于GO法的军事供应链可靠性分析

军事供应链管理要获得高效率和效益,最基础的必须是有高可靠性作保证。结合GO法——一种系统可靠性分析方法和军事供应链自身的特点,阐明了将GO法应用于军事供应链可靠性分析中的可行性。通过构建军事供应链结构模型,运用GO法原理,分析出军事供应链系统中的信号流和操作符,将军事供应链系统结构图转化为GO图,通过GO运算,求出各信号流的等效故障率,进而求出军事供应链中各节点的等效可靠度,对供应链网络结构模型的可靠性问题进行定量分析,并通过实例对该方法的应用进行了具体说明。结果表明：GO法不仅能够计算出整个军事供应链的可靠性,还可以清楚地得到各环节的可靠性,从而可以追溯出影响军事供应链系统整体可靠性的瓶颈,为制定相关的可靠性管理措施提供科学依据。     

基于FMECA的某型雷达系统故障分析

故障模式、影响及危害性分析 (简称FMECA)是提高系统可靠性的一种有效的工具。在雷达系统故障分析中运用FMECA方法 ,通过计算机辅助程序实现了故障分析的预测分析和定量分析。与传统的故障分析方法相比 ,该方法能够避免分析中的盲目性和主观性 ,可以获得更为准确的分析结果     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

装备结构演化规律及重要度分析

以装备设计模块化的现状为依据,运用重要度理论,分析了装备结构演化及其重要度排序的规律。从单一部件逐步演化到5部件结构,以及N部件串并联结构,在此基础上,对演化规律进行了梳理,并对典型结构进行了重要度分析,得到了关于装备结构演化与各部件重要度排序规律,填补了对装备重要度漂移规律的研究空白,为装备可靠性优化设计提供了技术支撑。