小子样条件下可靠性试验信息的融合方法

小子样条件下,不同状态的多源信息融合是可靠性工程领域的一个热门问题。综述了小子样条件下可靠性试验信息的融合方法。在一定的假设条件之下,利用分离可交换量模型对不同环境下的可靠性信息融合方法进行了研究,仿真算例说明这种融合方法充分利用了信息,评估结论更为合理。     

基于GO法的多阶段共因失效任务系统可靠性评估

为了解决部件阶段依赖性与共因失效给系统可靠性分析带来的复杂性,针对多阶段共因失效任务系统,结合GO法易于处理有时序、含共因失效系统可靠性分析的特点,首先,将前一阶段的输出信号作为后一阶段的输入信号,建模时首尾连接各阶段模型,建立整个任务系统的隐性GO模型;其次,在进行各信号流状态组合运算时,融入阶段代数处理部件的阶段依赖性;再次,用隐性共因失效分析,将共因失效对系统的影响在状态组合的定量分析中表达出来,得到所需系统可靠性指标;最后,与基于二元决策图的算法进行对比验证。结果表明:GO法可以简捷、准确地处理多阶段共因失效任务系统可靠性评估问题。     

基于RBF网络加速模型的可靠性评估

在可靠性加速试验中,由于各种不同的应力引起的失效机理不一样,不同应力之间可能还存在着相互耦合作用,要将它们和寿命结合,找出一个能真实描述客观情况的加速模型是相当困难的,仅仅依据有限的试验数据建立产品的加速模型存在很大难度和风险,为此提出了一种基于RBF网络加速模型的可靠性评估方法．该方法将加速寿命试验中的加速应力和可靠度作为训练网络的输入向量,相应的时间作为目标向量对网络进行训练,根据网络收敛速和误差精度等情况调整隐含层单元个数,直至得到最优的网络．最后,以实例说明了该方法的有效性和实用性．     

Evaluating cost and reliability integrated performance of stochastic logistics systems

The significance of integrating reliability into logistics performance has been established [The Logistics Performance Index and Its Indicators, World Bank International Trade and Transport Departments, (2010)]. Hence, as a response to the work by the World Bank, the present article aims to evaluate the performance index R_b,d of logistics systems as the probability that a specified demand d can be distributed successfully through multistate arc capacities from the source to the destination under the constraint that the total distribution cost should not exceed the cost limitation b. This article provides a pioneering approach for a straightforward computation of the performance index R_b,d. The proposed algorithm is a hybrid between the polynomial time capacity‐scaling algorithm, which was presented by Edmonds and Karp [JACM 19 (1972)], and the decomposition algorithm, which was presented by Jane and Laih [IEEE (2008)]. Currently, the proposed approach is the only algorithm that can directly compute R_b,d. An illustration of the proposed algorithm is presented. The results of the computational experiments indicate that the presented algorithm outperforms existing algorithms. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

逐步增加Ⅱ型截尾样本下k/N(G)系统可靠性指标的置信区间

在逐步增加Ⅰ型截尾样本下,研究k/N(G)系统可靠性指标的区间估计问题。假设部件寿命服从指数分布,利用极大似然法和Bayes方法,首先给出了部件失效率的Bayes近似置信区间,其次推导出了系统可靠度和平均寿命的Bayes近似置信区间的计算公式。最后给出随机模拟例子,并对置信区间的精度进行了讨论。     

水下传感器网络冗余设计

冗余设计可以有效地延长水下传感器网络寿命和提高网络可靠性,基于可靠性理论,考虑水下传感器网络环境及节点垂直移动等约束,在对冗余非共享方案研究的基础上,结合水下传感器网络特点,提出了适合于水下传感器网络特点的冗余共享设计方案,并给出了冗余共享方案的可靠性模型,通过数值计算,研究了网络可靠性变化曲线及冗余节点数与网络寿命的关系。经过MATLAB软件仿真,表明在同一网络寿命情况下,相对于冗余非共享方案,冗余共享方案所需冗余节点数更少,降低了网络部署成本,达到了冗余优化的目的。     

高可靠性软件的极值统计分析

提出了极值统计分析的方法.对于失效数据极值分布函数的拟合检验,采用了相关系数法;对于母体分布的参数估计,则分别采用了最小二乘估计(LSE)和极大似然估计(MLE);最后通过一个实例说明了统计过程,并用模拟的方法证明了最小二乘估计较好.     

基于威布尔过程模型的坦克部署期可靠性研究

通过对某新研坦克部署期故障情况进行具体而全面的统计分析,并采用基于随机点过程的可修系统理论中的威布尔过程模型(AMSAA模型),得出该型坦克部署期的可靠性特点与规律,对于研究新装备可靠性规律和提高新装备的部署保障水平,具有重要的借鉴作用.     

Evaluating methods for the reliability of a large 2‐dimensional rectangular k‐within‐consecutive‐(r,s)‐out‐of‐(m,n):F system

A 2‐dimensional rectangular k‐within‐consecutive‐(r, s)‐out‐of‐(m, n):F system consists of m × n components, and fails if and only if k or more components fail in an r × s submatrix. This system can be treated as a reliability model for TFT liquid crystal displays, wireless communication networks, etc. Although an effective method has been developed for evaluating the exact system reliability of small or medium‐sized systems, that method needs extremely high computing time and memory capacity when applied to larger systems. Therefore, developing upper and lower bounds and accurate approximations for system reliability is useful for large systems. In this paper, first, we propose new upper and lower bounds for the reliability of a 2‐dimensional rectangular k‐within‐consecutive‐(r, s)‐out‐of‐(m, n):F system. Secondly, we propose two limit theorems for that system. With these theorems we can obtain accurate approximations for system reliabilities when the system is large and component reliabilities are close to one. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Predictability of operational processes over finite horizon

Operational processes are usually studied in terms of stochastic processes. The main information measure used for predictability of stochastic processes is the entropy rate, which is asymptotic conditional entropy, thus not suitable for application over a finite horizon. We use the conditional entropy to study the predictability of stochastic processes over the finite horizon. It is well‐known that the conditional entropies of stationary processes decrease as the processes evolve, implying that, on average, their pasts become more informative about prediction of their future outcomes. Some important operational processes such as martingale, models for maintenance policies, nonhomogeneous Poisson, and mixed Poisson processes are nonstationary. We show that as a nonstationary process evolves, it may provide more information or less information about the future state of the system. We develop results for comparing the predictability of stochastic processes. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011