具有常规原因和人为错误的两个不同部件平行系统解的性质

讨论了一个由于常规原因和人为错误引起故障的两不同部件并行系统的模型,修复后的故障系统恢复正常.在假设修复率非常数的前提下,运用纯分析的方法给出了两不同部件并行系统解的性质,绕过了该系统解是否存在的问题.     

故障时间任意分布的两部件可修复系统解的适定性分析

讨论了一个由两个部件并联组成的可修复冗余系统模型,修复后的故障系统恢复如新.在假设修复函数有界的条件下,给出了C_0-半群的生成元(系统算子)对应的柯西问题的解的适定性分析.     

由常规故障和临界人为错误引起系统故障的可修复系统的稳定性分析

讨论了一个由两个部件和一个储备部件并且具有临界人为错误和常规故障的随机模型,故障系统修复时间是任意分布的.运用泛函分析的方法,通过分析系统主算子谱特征,给出了系统稳定性的证明.     

在常规故障和临界人为错误条件下具有易损坏储备部件复杂系统的可靠性分析

讨论了一个由两个部件和一个储备部件并且具有临界人为错误和常规故障的随机模型,研究了易损坏部件对系统的影响,故障系统的修复时间是任意分布的.运用泛函分析的方法,通过分析系统主算子的谱特征,给出了系统的可靠性分析的证明.     

具有单重休假和修复不如新的两部件并联系统

为了解决由"修复非新"部件组成的具有休假的可修型系统,运用几何过程理论、补充变量法和拉普拉斯变换工具,研究了由两个不同型部件和一个修理工组成的可修型并联系统.假设两个部件的工作寿命和修理时间均服从不同的指数分布,修理工可休假,对部件1的修理是几何修理而对部件2的修理则是修复如新,得到了系统的可用度、可靠度和系统首次故障前平均时间等可靠性指标.成果具有一定的理论和实际意义.     

具有备用部件的并联可修复系统稳态解的最优控制问题

针对一种具有两个运行部件和一个储备部件,考虑系统通常故障的发生,且系统故障服从一般分布的人—机系统模型.在Banach空间中,用泛数指标函数作为衡量系统可控的标准,给出了可修复系统最优控制的判别条件.     

可修C(2,3:F)系统的可靠性分析

考虑了由三个部件和一个维修工组成的线形可修系统.假定可修系统中的三个部件是相互独立的,每个部件的工作时间和维修时间均服从负指数分布.部件故障后不能修复如新以及关键部件具有优先维修权的情形下,利用几何过程与广义马尔可夫过程等数学工具对该系统的可靠性指标进行了深入的研究.我们得到了该系统的瞬时可用度,可靠度的L ap lace变换表达式.从而得到系统的稳态可用度及首次故障前的平均时间.为进一步探索线形可修系统、复杂串并联和复杂并串联系统提供了一条新途径.     

开关寿命连续型二部件温贮备可修系统的可靠性分析

本文对由2个部件组成的开关寿命为连续随机变量的温贮备可修系统,当部件的工作时间和维修时间以及转换开关的寿命和修理时间均服从指数分布,所有随机变量均相互独立,故障部件和转换开关能修复如新的情况下作了可靠性分析,建立了该类系统模型,给出了系统可靠度R（t)和首次故障前的平均时间MTTFF的解析表达式。     

一类具有可修复储备部件的人-机系统解的半离散化

针对一个具有两个运行部件和一个储备部件,考虑系统通常故障的发生,且系统故障修复时间服从一般分布的人-机系统模型,对系统模型中修复率用初等阶梯函数进行逼近,给出了系统的半离散化模型,为进一步数值计算打下理论基础.     

具有K个储备部件和N个故障状态可修复系统主算子性质

研究了具有N个故障状态和K个储备部件的可修复系统.将系统转化为Bancah空间中的Cauchy问题,分析了系统主算子的谱特征.运用正算子及共尾等理论,证明了系统主算子的增长界和谱上界相等.     

An interval-valued hesitant fuzzy multigranulation rough set over two universes model for steam turbine fault diagnosis

In the field of mechanical engineering, steam turbine fault diagnosis is a difficult task for mechanical engineers who are confronted with challenges in dealing with copious amounts of uncertain information. Different mechanical engineers may have their own opinions about the system fault knowledge base that differs slightly from other mechanical engineers. Thus, to solve the problems presented by uncertain data analysis and group decision-making in steam turbine fault diagnosis, we propose a new rough set model that combines interval-valued hesitant fuzzy sets with multigranulation rough sets over two universes, called an interval-valued hesitant fuzzy multigranulation rough set over two universes. In the multigranulation framework, both basic definitions and some important properties of the proposed model are presented. Then, we develop a general approach to steam turbine fault diagnosis by using the proposed model. Lastly, an illustrative example is provided to verify the established approach and demonstrate its validity and applicability.     

Fuzzy temporal fault tree analysis of dynamic systems

Fault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques.     

商业建筑人员疏散系统模型的构建与决策

针对我国商业建筑火灾事故频发、群死群伤事故严重的现状,对全国商业建筑特大火灾案例进行统计调查,建立人员疏散系统模型数据采用贝叶斯统计分析方法进行分析,克服了火灾数据缺乏、且难以进行大量重复实验的困难,得到各基本事件发生概率的条件期望估计与方差.通过误差传播对系统进行敏感性分析.通过对系统故障树的分析,得到在火灾发生情况下商业建筑人员疏散失败的概率及方差,并进行系统决策.     

Importance measures in reliability and mathematical programming

The importance measures have been a sensitivity analysis for a probabilistic system and are applied in diverse fields along with other design tools. This paper provides a comprehensive view on modeling the importance measures to solve the reliability problems such as component assignment problems, redundancy allocation, system upgrading, and fault diagnosis and maintenance. It also investigates importance measures in broad applications such as networks, mathematical programming, sensitivity and uncertainty analysis, and probabilistic risk analysis and probabilistic safety assessment. The importance-measure based methods are among the most practical decision tools.     

模糊技术在可靠性工程中的应用

本文主要介绍模糊集合理论和技术在可靠性工程中的应用现状及其发展趋势，尤其是在系统故障诊断，故障树分析（ＦＴＡ）系统可靠性分配，人的可靠性，软件可靠性，结构可靠性以及系统安全性工程中的应用和技术关键，并从工程应用的需要出发针对存在问题预测和提出各种解决途径。     

系统多故障状态模糊决策的研究

复杂系统发生故障时，往往出现两个或两个以上故障现象，并且故障原因与故障现象之间常存在交叉关系，传统的故障树分析法无法对系统多故障进行诊断。本将模糊决策的基本原理和故障树定量分析中常用的重要度分析法相结合，提出了一种对多故障状态的分析方法，并对液化气储配站烃泵灌装系统多故障状态进行了分析，取得了良好的应用结果。     

基于GA-PLS和AdaBoost的故障诊断方法

提出一种基于GA-PLS和AdaBoost的液压系统故障诊断方法。该方法用遗传算法与偏最小二乘法相结合(Genetic algorithm-partial least squares,GA-PLS)的算法对初始特征向量进行筛选,提取出与故障信息相关程度高的特征向量,把该特征向量作为输入,运用AdaBoost(Adaptive Boost)方法建立分类器,以识别液压系统的工作状态和故障类型。对实验数据分析的结果说明,该方法能准确地选择出特征向量,并有效地应用于液压系统的故障诊断。     

基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.     

Stabilization of active fault‐tolerant control systems by uncertain nonhomogeneous markovian jump models

This article investigates the stabilization and control problems for a general active fault‐tolerant control system (AFTCS) in a stochastic framework. The novelty of the research lies in utilizing uncertain nonhomogeneous Markovian structures to take account for the imperfect fault detection and diagnosis (FDD) algorithms of the AFTCS. The underlying AFTCS is supposed to be modeled by two random processes of Markov type; one characterizing the system fault process and the other describing the FDD process. It is assumed that the FDD algorithm is imperfect and provides inaccurate Markovian parameters for the FDD process. Specifically, it provides uncertain transition rates (TRs); the TRs that lie in an interval without any particular structures. This framework is more consistent with real‐world applications to accommodate different types of faults. It is more general than the previously developed AFTCSs because of eliminating the need for an accurate estimation of the fault process. To solve the stabilizability and the controller design problems of this AFTCS, the whole system is viewed as an uncertain nonhomogeneous Markovian jump linear system (NHMJLS) with time‐varying and uncertain specifications. Based on the multiple and stochastic Lyapunov function for the NHMJLS, first a sufficient condition is obtained to analyze the system stabilizability and then, the controller gains are synthesized. Unlike the previous fault‐tolerant controllers, the proposed robust controller only needs to access the FDD process, besides it is easily obtainable through the existing optimization techniques. It is successfully tested on a practical inverted pendulum controlled by a fault‐prone DC motor. © 2016 Wiley Periodicals, Inc. Complexity 21: 318–329, 2016     

输电系统的部分可观Petri网故障诊断器设计

采用部分可观Petri网的故障诊断方法来解决变电站输电系统中不可观事件和不可观运行状态的故障诊断问题.首先,将系统可观测序列分解为长度为1的基础观测序列,应用线性不等式矩阵计算与基础观测序列相符的点火序列集;然后,基于整数线性规划问题,利用向前向后函数拓宽诊断区间,同时应用参数K限定故障诊断序列长度,通过分析系统可观事件和系统部分可观状态,给出故障诊断结果.最后,构造变电站输电系统的部分可观Petri网模型,应用提出的故障诊断算法对输电系统进行诊断,诊断结果准确给出了故障发生与否及故障发生位置.算法适用于在线故障诊断,计算复杂性线性相关于观序列长度.