小子样多失效模式系统可靠性竞争模型

为解决小子样多失效模式系统可靠性模型参数的估计问题,首先应用支持向量积建立小子样条件下各故障模式的规律模型,其次利用模型将各故障模式的故障数据由小子样扩充成大样本,再次采用常规的参数估算方法估算各故障模式的分布参数,最后建立多失效模式系统的可靠性竞争模型。利用所提方法,建立了小子样条件下具有两种失效模式的柴油机汽缸套可靠性竞争模型,计算结果与大样本条件下的故障预测数据吻合度较高,说明了方法的有效性。     

基于FMECA的某型雷达系统故障分析

故障模式、影响及危害性分析 (简称FMECA)是提高系统可靠性的一种有效的工具。在雷达系统故障分析中运用FMECA方法 ,通过计算机辅助程序实现了故障分析的预测分析和定量分析。与传统的故障分析方法相比 ,该方法能够避免分析中的盲目性和主观性 ,可以获得更为准确的分析结果     

考虑故障影响的机构可靠性建模

对影响机构可靠性的主要因素、机构可靠性变化的基本过程以及表征机构可靠性变化的指标参数进行了分析 ,针对机构可靠性问题的特点 ,提出了基于机构输出参数的目标函数与实现函数的叠覆进行机构可靠性分析的方法以及考虑故障等级影响、对不同故障等级进行加权的机构可靠性失效判据 ,建立了与此相对应的机构可靠性分析的数学模型 ,给出了利用Monte Carlo方法对数学模型进行求解的计算流程 ,并对某实际工程问题进行了实例分析。     

气体灭火系统联动可靠性探讨

气体灭火系统用在特殊、重要的场所,其联动控制可靠性是成功灭火的关键.但由于设计单位、建设单位重视不够,影响了联动可靠性.为此针对气体灭火系统联动控制模式规范设计,严把施工质量关,搞好配套设备的控制,以确保系统联动控制可靠性.     

地(舰)空导弹弹体结构可靠性分析

对地(舰)空导弹弹体结构可靠性进行了分析,介绍了弹体结构可靠性的工程计算方法,提出了改进弹体结构可靠性的技术途径。     

Reliability of a 2‐dimensional k‐within‐consecutive‐r × s‐out‐of‐m × n:F system

A 2‐dimensional rectangular (cylindrical) k‐within‐consecutive‐r × s‐out‐of‐m × n:F system is the rectangular (cylindrical) m × n‐system if the system fails whenever k components in a r × s‐submatrix fail. This paper proposes a recursive algorithm for the reliability of the 2‐dimensional k‐within‐consecutive‐r × s‐out‐m × n:F system, in the rectangular case and the cylindrical case. This algorithm requires min ( O (mk^r(n?s)), O (nk^s(m?r))), and O (mk^rn) computing time in the rectangular case and the cylindrical case, respectively. The proposed algorithm will be demonstrated and some numerical examples will be shown. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 625–637, 2001.     

Asymptotically optimal component assembly plans in repairable systems and server allocation in parallel multiserver queues

T identical exponential lifetime components out of which G are initially functioning (and B are not) are to be allocated to N subsystems, which are connected either in parallel or in series. Subsystem i, i = 1,…, N, functions when at least K_i of its components function and the whole system is maintained by a single repairman. Component repair times are identical independent exponentials and repaired components are as good as new. The problem of the determination of the assembly plan that will maximize the system reliability at any (arbitrary) time instant t is solved when the component failure rate is sufficiently small. For the parallel configuration, the optimal assembly plan allocates as many components as possible to the subsystem with the smallest K_i and allocates functioning components to subsystems in increasing order of the K_i's. For the series configuration, the optimal assembly plan allocates both the surplus and the functioning components equally to all subsystems whenever possible, and when not possible it favors subsystems in decreasing order of the K_i's. The solution is interpreted in the context of the optimal allocation of processors and an initial number of jobs in a problem of routing time consuming jobs to parallel multiprocessor queues. © John Wiley & Sons, Inc. Naval Research Logistics 48: 732–746, 2001     

An optimal structured policy for maintenance of partially observable aircraft engine components

This paper considers the maintenance of aircraft engine components that are subject to stress. We model the deterioration process by means of the cumulative jump process representation of crack growth. However, because in many cases cracks are not easily observable, maintenance decisions must be made on the basis of other information. We incorporate stress information collected via sensors into the scheduling decision process by means of a partially observable Markov decision process model. Using this model, we demonstrate the optimality of structured maintenance policies, which support practical maintenance schedules. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 335–352, 1998     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

Spatial pricing and product allocation in online retailing

Spatial pricing means a retailer price discriminates its customers based on their geographic locations. In this article, we study how an online retailer should jointly allocate multiple products and facilitate spatial price discrimination to maximize profits. When deciding between a centralized product allocation ((i.e., different products are allocated to the same fulfillment center) and decentralized product allocation (ie, different products are allocated to different fulfillment centers), the retailer faces the tradeoff between shipment pooling (ie, shipping multiple products in one package), and demand localization (ie, stocking products to satisfy local demand) based on its understanding of customers' product valuations. In our basic model, we consider two widely used spatial pricing policies: free on board (FOB) pricing that charges each customer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. We propose a stylized model and find that centralized product allocation is preferred when demand localization effect is relatively low or shipment pooling benefit is relatively high under both spatial pricing policies. Moreover, centralized product allocation is more preferred under the FOB pricing which encourages the purchase of virtual bundles of multiple products. Furthermore, we respectively extend the UD and FOB pricing policies to flat rate shipping (ie, the firm charges a constant shipping fee for each purchase), and linear rate shipping (ie, the firm sets the shipping fee as a fixed proportion of firm's actual fulfillment costs). While similar observations from the basic model still hold, we find the firm can improve its profit by sharing the fulfillment cost with its customers via the flat rate or linear rate shipping fee structure.