Lognormal Approximations of Fault Tree Uncertainty Distributions

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed‐form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling‐based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed‐form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks’s method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models.     

论间接故意与过于自信的过失 --理论与实际的矛盾

间接故意与过于自信的过失是犯罪构成中主观方面最容易混淆的两种罪过,本文对这两种罪过进行了系统的比较,并对两者的实际操作中所体现的矛盾进行了分析,最后文章对矛盾提出了解决的方案。     

论缔约过失责任制度——兼评合同自由与合同正义

缔约过失责任制度的产生与发展是 19世纪下半叶尤其是 2 0世纪以来合同法走向现代化的重要标志之一。缔约过失责任制度的价值论基础 ,就在于诚实信用原则所体现的正义价值与合同自由原则所体现的自由价值构成相倚的两极 ,产生相互亲和的张力。加强对我国缔约过失责任制度的深入研究 ,评析其得失 ,有助于进一步完善我国缔约过失责任制度     

基于规则的MCD磨粒信息诊断专家系统

研究了一种用于发动机磨损状态监测与故障诊断的MCD磨粒信息诊断专家系统 (ES)。首先讨论了诊断知识的获取与规则表示方法 ,接着根据面向对象模板化设计思想 ,研究了一种知识库、推理机和人机界面的一体化构造策略 ,并在此基础上引入深度优先搜索、反向引导正向控制的启发性推理机制     

Wide Diameters of Cartesian Product Graphs and Digraphs

In graph theory and study of fault tolerance and transmission delay of networks, connectivity and diameter of a graph are two very important parameters and have been deeply studied by many authors. Wide diameter combining connectivity with diameter is a more important parameter to measure fault tolerance and efficiency of parallel processing computer networks and has received much attention in the recent years. Diameter with width k of a graph G is defined as the minimum integer d for which between any two distinct vertices in G there exist at least k internally disjoint paths of length at most d. In the present paper, the tight upper bounds of wide diameter of the Cartesian product graphs are obtained. Some known results can be deduced or improved from ours.     

套管的失效分析

通过收集石油套管现场失效数据资料,以套管失效为顶事件建立了石油套管的故障树,该故障树共考虑了47个不同的基本事件。通过对套管故障树的分析,得到了套管失效故障树的一阶最小割集27个,二阶最小割集29个,四阶最小割集1个,确立了套管的主要失效形式为潜在损坏、套管挤毁、套管断裂及严重腐蚀,并提出了提高套管可靠性的措施。     

基于支持向量机的管理信息系统故障识别模型研究

管理信息系统故障影响着企业信息工程的开展。及时识别故障,能够为企业争取更多时间处理故障。因此,管理信息系统故障识别有着重要的研究意义和实践意义。基于支持向量机,构建管理信息系统故障识别模型,通过把样本应用于该模型,结果表明该模型具有较好的准确性,能有效识别管理信息系统故障。     

高含水期复杂断块油藏剩余油分布及挖潜

高含水期陆相复杂断块层状油藏剩余油分布复杂,油藏模拟难度大。马11复杂断块油藏在油藏精细描述基础上,利用动静态综合分析研究与密网格大规模数值模拟研究相结合,提高了剩余油研究精度和可信度;在此基础上,针对油藏剩余油分布特点并结合当前经济技术条件,部署实施了以调整井、调剖、堵水、油水井大修为主的剩余油挖潜方案,油藏含水上升率大幅度下降、采油速度上升、采收率提高了。     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.