依赖团数的有向图极大与超级边连通的度序列条件

互连网络通常以有向图为模型,有向图的弧连通度是网络可靠性的一个重要参数.给出了依赖团数的有向图极大和超级边连通的度序列条件.     

FTA法和重要度分析在某系统可靠性中的应用

应用失效树分析（Fault Tree Analysis）法（简称FTA法）,对某型火炮遥控系统进行了可靠性分析,建立了该系统的失效树,并在此基础上计算了该系统的可靠性参数,讨论了部件的概率重要度和相对比重要度,并把部件的概率重要度和相对比重要度推广到了分系统,结合所建的失效树给出了相应的结果。通过重要度分析,找到了提高系统可靠性中的途径。     

折叠交叉立方体的分支边连通度

r-分支连通度(边连通度)是衡量大型互连网络可靠性和容错性的一个重要参数.设G是连通图且r是非负整数,如果G中存在某种点子集(边子集)使得G删除这种点子集(边子集)后得到的图至少有r个连通分支.则所有这种点子集(边子集)中基数最小的点子集(边子集)的基数称为图G的r-分支连通度(边连通度).n-维折叠交叉立方体FCQn是由交叉立方体CQn增加2n-1条边后所得.该文利用r-分支边连通度作为可靠性的重要度量,对折叠交叉立方体网络的可靠性进行分析,得到了折叠交叉立方体网络的2-分支边连通度,3-分支边连通度,4分支边连通度.确定了折叠交叉立方体FCQn的r-分支边连通度.     

相关性失效下的零部件重要度统计评价

重要度评价在可靠性工程中有着举足轻重的地位,是产品可靠性设计的基础.分别研究了在相依部件系统中的部件可靠性重要度与结构重要度.采用多维Copula函数拟合多部件之间的相依结构,从各类型重要度的刻画角度,经过一系列的数学处理,建立相应的相关性失效下零部件重要度评价模型.对于复杂且实用的k/n(G)系统,运用可靠度计算与结构函数表征之间的等效映射来对相关性失效下的三类重要度评价进行建模.     

基于模糊贝叶斯网络的汽车软关系统可靠性研究方法和优化

为解决传统方法在状态多样化的复杂系统可靠性分析中的局限性,通过贝叶斯网络的推理功能得到系统故障概率与可靠度评估、后验概率及根节点重要度的计算和分析的方法,充分利用了贝叶斯网络不确定性推理和图形化表达的优势.文章以汽车软关系统为研究对象,在系统可靠性框图模型的基础上,应用新方法计算了各根节点失效概率和重要度并进行了排序,针对系统的薄弱环节,提出了相应的改进方案,为系统的产品和类似系统的可靠性研究提供了理论参考,具有较强的工程实用价值.     

供应链网络可靠性的多层Bayes估计模型

对供应链网络可靠性进行界定和分析,以节点企业间的协同为基础,得到可靠度计算模型,以此为依据采集实证样本无失效运行的数据.根据供应链网络可靠性统计特性,建立一种多层Bayes估计方法,应用于样本可靠性评估中.在估计失效率的基础上,对供应链网络可靠度进行估计,对仿真结果进行分析,显示多层Bayes估计方法应用效果较好,精确度高,反映了参数不确定性对供应链网络可靠性的影响,能够较好地解决依据无失效数据判定供应链网络可靠性水平的问题.     

结构的失效可能度及模糊概率计算方法

依据模糊可能性理论,系统地建立含模糊变量时结构的可靠性计算模型。旨在解决模糊结构、模糊-随机结构和模糊状态假设下结构的可靠性计算问题。所建模型可给出模糊结构失效的可能度和模糊-随机结构失效概率的可能性分布。研究表明:对同时含模糊变量和随机变量的混合可靠性计算问题,把失效概率(或可靠度)作为模糊变量,能更客观地反映系统的安全状况。算例分析说明了文中方法的合理性和有效性。     

可靠性数学中的若干问题和进展

本文将综述可靠性数学理论中的一些重要问题和进展,其中包括网络可靠度计算、多状态coherent系统理论以及可修系统理论.由于篇幅限制,文中论题并不概括可靠性数学理论的全貌,文献也是基本的.一、网络可靠度网络系统可靠度的计算,特别是对大型复杂系统寻找有效的算法,以及各类算法优劣的比较,一直是理论和实用上都感兴趣的问题.1.基本问题给定一个网络(图)G,其中节点(顶点)集合V={v_i,…,v_n},弧(边)的集合E={e_l,…,e_b}。假定节点与弧只有正常和失效两种状态,并进一步设失效之间是互相独立的.(a)有源问题记S、t为两个特定的节点,称作“发点”和“收点”,求     

考虑节点综合重要度的物流网络级联失效模型

为提高突发事件级联失效对物流网络破坏程度的评估的可信性,提出一个考虑物流网络边权特征的节点重要度的综合度量方法,并在此基础上构建相应的级联失效模型。数值仿真结果表明：该模型对于物流节点重要度的衡量更为完善,所制定的失效负载分流准则及其对于级联失效破坏性的评估结果更具合理性。通过该模型可更加全面地掌握了解网络结构对于级联失效破坏的抵御能力,为物流应急管理提供理论支持。     

完全对换网络的限制连通度

完全对换网络是基于 Cayley 图模型的一类重要互连网络. 一个图 G 的 k-限制点(边)连通度是使得 G-F 不连通且每个分支至少有 k 个顶点的最小点(边)子集 F 的基数, 记作 \kappa_{k}(\lambda_{k}). 它是衡量网络可靠性的重要参数之一, 也是图的容错性的一种精化了的度量. 一般地, 网络的 k-限制点(边)连通度越大, 它的连通性就越好. 证明了完全对换网络 CT_{n} 的 2-限制点(边)连通度和 3-限制点(边)连通度, 具体来说: 当 n\geq4 时, \kappa_{2}(CT_{n})=n(n-1)-2, \kappa_{3}(CT_{n})=\frac{3n(n-1)}{2}-6; 当 n\geq3 时, \lambda_{2}(CT_{n})=n(n-1)-2, \lambda_{3}(CT_{n})=\frac{3n(n-1)}{2}-4.     

基于可靠性弹性指标的系统弹性度量方法

为深入研究系统弹性问题,首先分析系统中不同节点和边的可靠性弹性指标。通过引入节点弹性与边弹性,并考虑节点弹性与边弹性二者的联系以及对系统弹性的影响,建立了能够反映系统拓扑结构变化的弹性度量方法模型。最后,在对弹性度量方法的验证环节中,引入了具有分层特性的交通系统,按照所述弹性度量方法对分层交通系统中的节点弹性以及边弹性进行了分析,发现该种分层交通系统的整体弹性程度一般,任何小的干扰或者故障都有可能造成该交通系统拥堵或瘫痪。     

Monte-Carlo algorithms for the planar multiterminal network reliability problem

This paper presents a general framework for the construction of Monte-Carlo algorithms for the solution of enumeration problems. As an application of the general framework, a Monte-Carlo method is constructed for estimating the failure probability of a multiterminal planar network whose edges are subject to independent random failures. The method is guaranteed to be effective when the failure probabilities of the edges are sufficiently small.     

Optimally reliable networks

In this paper, we consider the probability of disconnection of a graph as a measure of network reliability. We compare the vertex and edge failure cases, and then concentrate on the vertex failure case. Optimal graphs are graphs which minimise the probability of disconnection for a given number of vertices and edges when the probability of vertex failure is small. We describe the known results on the construction of optimal regular graphs and present some new results on the construction of optimal nonregular graphs.     

Key potential-oriented criticality analysis for complex military organization based on FINC-E model

The complex social organizations, which can self-organize into the region “at the edge of chaos”, neither too ordered nor too random, now have become an interdisciplinary research topic. As a kind of special social organization, the complex military organization usually has its key entities and relations, which should be well protected in case of attacks. In order to do the criticality analysis for the military organization, finding the key entities or relations which can disrupt the functions of the organization, two problems should be seriously considered. First, the military organization should be well modeled, which can work well in the specialized military context; secondly it is critical to define and identify the key entities or relations, which should incorporate the topological centrality and weighted nodes or edges. Different from the traditional military organizations which are usually task-oriented, this paper proposes the Force, Intelligence, Networking, and C2 Extended (FINC-E) Model for complex military organization, with which a more detailed and quantitative analysis for the military organization is available. This model provides the formal representation for the nodes and edges in the military organization, which provides a highly efficient and concise network topology. In order to identify the critical nodes and edges, a method based on key potential is proposed, which acts as the measurement of criticality for the heterogeneous nodes and edges in the complex military organization. The key potential is well defined on the basis of topology structure and of the node’s or edge’s capability, which helps to transform the organization from the heterogeneity to the homogeneity. In the end, the criticality analysis case study is made for both small-world networked military organization and scale-free networked military organization, showing that the measure of key potential has the advantage over other classical measures in locating the key entities or relations for complex military organization.     

Sixty Years of Network Reliability

The study of network reliability started in 1956 with a groundbreaking paper by E.F. Moore and C.E. Shannon. They introduced a probabilistic model of network reliability, where the nodes of the network were considered to be perfectly reliable, and the links or edges could fail independently with a certain probability. The problem is to determine the probability that the network remains connected under these conditions. If all the edges have the same probability of failing, this leads to the so-called reliability polynomial of the network. Sixty years later, a lot of research has accumulated on this topic, and many variants of the original problem have been investigated. We review the basic concepts and results, as well as some recent developments in this area, and we outline some important research directions.     

A method for maximizing the reliability coefficient of a communications network

The most common idea of network reliability in the literature is a numerical parameter calledoverall network reliability, which is the probability that the network will be in a successful state in which all nodes can mutually communicate. Most papers concentrate on the problem of calculating the overall network reliability which is known to be an NP hard problem. In the present paper, the question asked is how to find a method for determining a reliable subnetwork of a given network. Givenn terminals and one central computer, the problem is to construct a network that links each terminal to the central computer, subject to the following conditions: (1) each link must be economically feasible; (2) the minimum number of links should be used; and (3) the reliability coefficient should be maximized. We argue that the network satisfying condition (2) is a spanning arborescence of the network defined by condition (1). We define the idea of thereliability coefficient of a spanning arborescence of a network, which is the probability that a node at average distance from the root of the arborescence can communicate with the root. We show how this coefficient can be calculated exactly when there are no degree constraints on nodes of the spanning arborescence, or approximately when such degree constraints are present. Computational experience for networks consisting of up to 900 terminals is given.This report was prepared as part of the activities of the Management Science Research Group, Carnegie-Mellon University, under Contract No. N00014-82-K-0329 NR 047–048 with the U.S. Office of Naval Research. Reproduction in whole or in part is permitted for any purpose of the U.S. Government.     

A computational framework of kinematic accuracy reliability analysis for industrial robots

A new computational method to evaluate comprehensively the positional accuracy reliability for single coordinate, single point, multipoint and trajectory accuracy of industrial robots is proposed using the sparse grid numerical integration method and the saddlepoint approximation method. A kinematic error model of end-effector is constructed in three coordinate directions using the sparse grid numerical integration method considering uncertain parameters. The first-four order moments and the covariance matrix for three coordinates of the end-effector are calculated by extended Gauss–Hermite integration nodes and corresponding weights. The eigen-decomposition is conducted to transform the interdependent coordinates into independent standard normal variables. An equivalent extreme value distribution of response is applied to assess the reliability of kinematic accuracy. The probability density function and probability of failure for extreme value distribution are then derived through the saddlepoint approximation method. Four examples are given to demonstrate the effectiveness of the proposed method.     

The influence of the node criticality relation on some measures of component importance

For different reliability importance measures we prove that the criticality relation between nodes can completely determine the most important component in a system. In particular, we prove that in k-out-of-n systems, the ranking of component reliabilities determines the ranking of component importance for, at least, three different reliability importance measures.     

考虑n中连续取k失效准则的树状系统可靠性求解方法

树状网络系统在管道运输,网络通信中较为常见,对其进行可靠性评估对系统设计及优化具有重要意义。针对树状冗余系统,在n中连续取k失效准则下,通过有限马尔可夫嵌入法并对其进行变形,研究了树状系统可靠性求解方法。本文对树状系统建模加以定义,提出了基于层数参数,层-节点向量,父-子节点矩阵三元参数的树状系统表示方法,研究了变形有限马尔可夫嵌入法的树状系统n中连续取k失效准则下的可靠性求解方法,给出了三个数值算例应用并分析了算法的运算复杂度。最后,本文对比讨论了基于概率母函数法的树状系统在n中连续取k准则下系统可靠性求解方法的研究,得出结论本文算法针对树状冗余系统n中连续取k失效准则下系统可靠性求解应用范围更广,求解效率较高。     

Hierarchical approach for survivable network design

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.