雷达与导航、声纳与对抗

0104316备件需求量计算模型及其在地面雷达中的应用[刊]/杨秉喜//电子产品可靠性与环境试验—2000.(6).—13～17(E)提供了4种寿命分布件备件需求量计算模型,分别是指数寿命件、威布尔寿命件、正态寿命件、战伤件二项分布计算模型。并给出了在地面雷达备件供应中的应用示例。参2     

考虑储备期间失效的可修复备件需求模型

备件数量决定了装备维修能力和保障水平,能否科学地预测备件数量,取决于备件需求计算模型建立的准确性。在此根据备件维修性将备件分为可修复件和不可修复件,针对可修复件的备件需求问题,在考虑储备期间发生故障的前提下,利用备件可靠性框图方法,建立单可修复件、多可修复件的备件需求计算模型,解决了备件考虑储备期间故障的实际需求。结合工作环境的影响对模型中的故障率进行修正,使模型更贴近实际需求。     

基于寿命更换策略确定备件需求量

为确保部队的战备值班任务,针对定时更换备件预防性维修的研究比较缺乏,文章介绍一种较实用的定时更换预防性维修策略即寿命更换策略,为以后的研究提供借鉴作用。文章分析了该策略的模型导出和确定备件需求量的方法,计算出备件需求量,最后给出计算实例,并通过仿真验算。     

备件保障度评估与备件需求量模型研究

基于更新过程理论,给出了部件寿命服从不同分布(指数分布、威布尔分布、正态分布和Г分布)情况下,单不可修部件的备件保障度模型;然后给出了多不可修部件和多可修部件的备件保障度模型．通过具体算例,验证了模型的正确性;最后在备件保障度模型的基础上,给出了备件需求量模型。     

雷达可修复备件优化配置研究

备件保障是提高装备的战备完好性和降低寿命周期费用的重要研究领域.文中研究了可修复备件保障建模方法及应用,基于METRIC模型基本理论,并结合雷达装备备件保障现状,建立了雷达可修复备件优化配置模型.模型采用备件期望短缺数作为模型的优化目标,运用边际效能法优化备件配置,并结合实例进行了应用.     

面向任务的备件携行量仿真优化方法

在多阶段任务系统中，可修部件的寿命服从一般分布、维修时间也服从一般分布的备件携行量优化问题很难利用解析方法来解决。采用仿真的方法，建立可修部件寿命和维修时间均服从一般分布的多阶段任务系统的仿真模型，给出了最优备件携行量的仿真计算方法，讨论了如何确定维修组的数量。并且通过实例检验了这种仿真方法的适用性。最后利用一个指数分布实例对解析方法和仿真方法计算出的结果进行比较，从而对所提出的仿真方法进行了检验。     

机载装备分系统级机电产品贮存备件研究

对空空导弹基层级备件的计算模型进行了研究.并通过工程实例,对基层可更换单元弹上机电分系统级备件的计算模型进行分析,提出应按正态分布模型进行空空导弹分系统级备件计算的结论.     

某火控系统备件需求计算方法研究

研究装备备件需求计算方法,为某自行火炮火控系统备件保障提供决策依据。通过研究备件在装备保障中的重要性,分析了影响装备备件需求的因素,讨论了基于贝叶斯的某自行火炮火控系统的备件需求计算法。从而得出了基于贝叶斯的某自行火炮火控系统备件需求预测计算模型。对于随机波动、变化相对稳定的指标,用贝叶斯模型预测是比较精确的,在实际工作中具有可行性。     

备件需求量计算模型分析

科学合理地解决备件配置问题一直为人们所瞩目 ,如何科学地确定备件数量 ,备件需求量计算模型的选择尤其重要。按照备件的不同种类选择不同的计算模型 ,是本文的主要观点。     

备件需求量计算模式分析

科学合理地解决备件配置问题一直为人们所瞩目,如何科学地确定备件数量,备件需求量计算模型的选择尤其重要。按照备件的不同种类选择不同的计算模型,是本文的主要观点。     

基于战备完好性的初始备件供应保障Monte-Carlo仿真

备件需求量和供应时间是备件供应保障过程的核心问题。按照战备完好性要求,建立了使用可用度与备件保障概率关系模型。采用Mento-Carlo法,对以战备完好性为中心的备件需求量、平均后勤延误及备件短缺风险进行了仿真,仿真结果对提高部队的综合保障能力,确定影响初始备件供应的原因具有指导作用。     

Logistics Availability for Sites with Periodic or Random Resupply

Logistics Availability (AL), the probability that a system is not in a spares delay downstate at any instant of time, is a basic element of Operational Availability. Formulas for AL for serial systems of assemblies are derived. Each assembly has exponential times to failure and is supported by a spares inventory that is either periodically resupplied or resupplied as-needed (an order is placed following each failure which requires a spare to restore the system). The formulas depend upon the reliability of the operating assemblies, the specified number of spares in the full-up inventory, the usual resupply times, and the constant spares delay downtime for failures that occur when there are no spares in the inventory. These formulas for AL can be used in well-known integer optimizing processes to select the spares quantities for the site inventory and/or to determine acceptable resupply periods and spares delay downtime, in addition to assessing AL.     

Determining optimal redundancy for systems with random lifetimes

The problem of allocating spares to remote machines is examined. No reallocation of the spares between machines is possible, and the life distribution of each spare depends upon the machine on which it is used. The machines are subsystems of a system whose useful life terminates in a finite, but random, amount of time (e.g., from a catastrophe or obsolescence), or when a machine depletes its store of spares. An efficient algorithm determines the allocation of spares that maximizes the minimum probability of a machine depleting its spares before the system's useful life terminates. These results are extended to the case when the spares are divisible. An example illustrates the results     

A New Technique for Depot and Sub-Depot Spares

This paper presents work in analyzing the combined spares provisioning implementation for depot and sub-depots. A method was developed for obtaining the minimum number of depot plus sub-depot spares. Not only is the number of spares important, but their cost as well. In order for the sub-depot model to achieve the savings in spares, a large number of depots (depots plus sub-depots) will be required than is required by the depot model, but the total capacity of this larger number of depots will be less than the capacity of the depots using the simple depot model. Therefore, one must not only study the numbers of spares and their cost, but must also account for the cost of the depots and sub-depots in order to truly optimize the design.     

雷达装备可修复备件三级库存优化仿真

针对雷达装备器材供应保障中可修复备件配置问题,在分析三级保障结构下可修复备件运转流程的基础上,构建了可修复备件三级供应保障结构下库存优化模型。模型采用基层级供应可用度作为优化目标,以备件订购费用作为约束条件,运用边际分析法优化可修复备件三级库存配置,实现了在有限购置费用约束条件下的基层级供应可用度最大化。最后,通过实例对三级库存模型进行了仿真分析,并评估了中继级维修时间、基地级补给时间等因素对基层级供应可用度的影响,仿真结果可为雷达装备可修复备件合理配置提供参考。     

Availability calculations with exhaustible spares

Many systems rely solely on the spares they carry to fulfil their missions. The authors develop relatively simple equations for the availability of a system with exhaustible spares. The equations are conservative but, for a large system, are more tractable than simulation or the exact approach based on Markov theory. The equations are useful for tradeoff or sensitivity analysis. Given various complements of spares, system availability can be calculated, or the optimal selection of spares can be determined. Since the equations conservatively approximate system availability if the system consists of several types of units in series, the equations can be used to determine if a system meets its availability requirement. If the calculated system availability is less than the requirement, spares could be added or more exact techniques could be applied. Because of various simplifying assumptions, the equations are most exact when repair time is small compared to mission time and when k is close to n for k-out-of-n:G systems     

备件保障性的综合与分配模型

备件保障性是装备战备完好性的重要因素。首次给出了备件保障性的综合和分配模型。为系统备件保障性预计和设计创造了前提条件。同时为完善系统的战备完好性的工程设计奠定基础。     

Reliability Predictions and System Support Costs

A case study demonstrates that spares requirements based on operational reliability predictions result in the system being undersupported during its burn-in and useful-life phases by average factors of 42.2 and 4.22, respectively. However, systems spares requirements based on logistics estimates result in the system being oversupported during its burn-in phase by an average factor of 4.26 with this factor probably increasing as the system enters the useful-life phase of its life cycle. Adequate system support within predetermined cost estimates will be achieved only when reliability predictions are accurate and when systems spares requirements are computed separately for the equipment burn-in phase and useful-life phase.