Bayesian inference based on partial monitoring of components with applications to preventive system maintenance

Consider a binary, monotone system of n components. The assessment of the parameter vector, θ, of the joint distribution of the lifetimes of the components and hence of the reliability of the system is often difficult due to scarcity of data. It is therefore important to make use of all information in an efficient way. For instance, prior knowledge is often of importance and can indeed conveniently be incorporated by the Bayesian approach. It may also be important to continuously extract information from a system currently in operation. This may be useful both for decisions concerning the system in operation as well as for decisions improving the components or changing the design of similar new systems. As in Meilijson [12], life‐monitoring of some components and conditional life‐monitoring of some others is considered. In addition to data arising from this monitoring scheme, so‐called autopsy data are observed, if not censored. The probabilistic structure underlying this kind of data is described, and basic likelihood formulae are arrived at. A thorough discussion of an important aspect of this probabilistic structure, the inspection strategy, is given. Based on a version of this strategy a procedure for preventive system maintenance is developed and a detailed application to a network system presented. All the way a Bayesian approach to estimation of θ is applied. For the special case where components are conditionally independent given θ with exponentially distributed lifetimes it is shown that the weighted sum of products of generalized gamma distributions, as introduced in Gåsemyr and Natvig [7], is the conjugate prior for θ. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 551–577, 2001.     

A risk function for the stochastic modeling of electric capacity expansion

We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate‐size problems are presented along with comparison to a general‐purpose nonlinear optimization package. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:662–683, 2001     

一种双参数批更换系统的优化问题

研究了一种在更换时间有限的情况下 ,基于交替更新过程的双参数批更换系统的优化问题 ,这一问题的目标是平均费用率的极小化。给出了一种特殊情况的讨论结果。     

粗集理论在目标识别信息处理中的应用

为提取传感器获得的较粗糙的原始信息,运用粗集理论进行目标识别信息的处理,提出了一种采用关系表存储原始信息,通过简化关系表删去冗余信息,从而达到提取有用信息的处理方法.根据此方法,实例中通过对生成的规则进行优选,得到了简单准确的目标识别规则.此理论在信息处理中的应用,较好地满足了目标识别中原始信息处理的需求.     

备件保障效能动态评估方法

针对目前效能评估方法多重视效能指标的静态观测值,对时序状态数据所蕴含的趋势信息关注较少的缺点,提出基于灰色聚类-粗糙集和集对分析的备件保障效能动态评估方法。针对主客观赋权方法各自的优缺点,引入依赖度和重要度的概念,建立灰色聚类-粗糙集组合赋权模型;将指标权重引入集对理论,提出集对同势、均势和反势的定义,描述备件保障效能的变化规律,构建基于马尔可夫链的集对分析动态模型。实例分析结果表明,该方法可以有效反映备件保障效能的动态变化特征,为决策者制定备件保障长期计划提供科学依据。     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

网格质量反馈的弹性体动网格改进

高鲁棒性的动网格变形方法是求解包含运动边界的非定常流场问题的关键技术。基于经典的弹性体动网格方法,引入两种设计合理、评价效果较好的网格质量参数——Patrick参数和Baker参数,提出了一种基于网格质量反馈的改进弹性体动网格方法。结合二维NACA0012翼型、三维ONERA M6机翼大角度俯仰运动,对比研究改进方法与经典方法的变形能力,改进方法能够保持较好的网格质量,尤其在最大变形时,可以有效改善较差网格单元,具有更高的鲁棒性。计算了NACA0012翼型、ONERA M6机翼的俯仰振荡绕流,并与实验结果及文献进行对比,结果表明,基于网格质量反馈的改进弹性体法能够显著提高变形网格的鲁棒性,可为非定常流场问题的求解提供有力支撑。     

基于网格质量反馈的弹性体动网格改进研究

高鲁棒性的动网格变形方法是求解包含运动边界的非定常流场问题的关键技术。基于经典的弹性体动网格方法,引入两种设计合理、评价效果较好的网格质量参数——Patrick网格质量参数和Baker网格质量参数,从网格质量反馈优化的角度对经典方法作出改进,提出了一种基于网格质量反馈的改进弹性体动网格方法。结合NACA0012翼型的大角度俯仰运动,对比研究改进方法与经典方法的变形能力,整个过程中改进方法均保持更好的平均/最小网格质量,尤其在大俯仰角90deg时,经典方法的平均、最小网格质量降幅均是改进方法的4倍之多,且机翼尾缘处的网格扭曲严重,而改进方法仍与初始网格保持了较好的一致性,具有更高的鲁棒性。作为例证,分别计算了二维NACA0012翼型、三维ONERA M6机翼的俯仰振荡绕流,并与实验结果及流场规律进行对比,结果表明：基于网格质量反馈的改进弹性体法能够显著提高变形网格的鲁棒性,在大幅度的变形下保持较高的网格质量,对多维流场的求解均可取得精确结果,可为非定常流场问题的求解提供有力支撑。     

用不确定性度量作战效能的评估方法

把作战过程分解成多个节点,用不确定性自信息量度量各节点完成任务的程度,并改造了信息熵函数,得出一种适合求解作战效能的评估方法.最后,针对一实例进行效能评估建模、验模和灵敏度分析.     

一种新型粗糙集神经网络的故障诊断方法

结合粗糙集理论,建立了基于SOFM网络的新型粗糙集神经网络故障诊断模型,给出了该模型的流程图,描述了系统各组成部分的工作原理.应用该模型对某型雷达进行故障诊断,实例证明新模型能够减少输入端数量,简化神经网络的结构,提高系统的速度,诊断时间短,准确性高,结果易于实现可视化,最后得到了故障可视拓扑映射图.