一般概率分布下考虑预防性维修的ADC 模型可信赖度

克服了马尔可夫假设条件的限制,假定系统寿命、修复性维修和预防性维修的修复时间均遵从一般概 率分布．利用离散时间模型在数值计算方面的优势,建立了离散时间下系统正常工作、修复性维修和预防性维修三 个状态之间的转移关系,在此基础上建立了一般概率分布下考虑预防性维修的ADC 模型的可信赖度D．数值算例 说明了该评估方法有助于选择合适的预防性修复周期来提高系统的效能．     

基于ADC模型的通信网络效能评估方法研究

针对通信网络性能指标多、效能评估困难的问题,提出了基于ADC模型的效能评估方法,并采用可视化的方法实现。模型选取可用性行向量、可信赖性矩阵和通信保障能力作为关键指标,能够充分体现通信网络的特点进行科学评估。理论分析和实验测试证明了所提出的方法能够有效地评估复杂的网络性能指标,并给管理人员提出合理的决策依据。     

基于ADC法的通信系统效能评估研究

为了架构合理有效的舰船通信保障体系,该文参考系统效能定义,研究了效能评估的理论,在分析效能评估方法的优缺点之后,选择ADC方法对通信系统的通信效能进行了分析,在设置相应参数的基础上进行计算,得到了有一定意义的结果。论文对评估结果进行了分析和讨论,得到的结论对制定有效的舰船通信方案具有一定的参考价值。     

基于ADC方法的炮射导弹系统效能评估

为研究炮射导弹系统效能,提高炮射导弹的综合质量特性,基于ADC（availability dependability capability）方法对炮射导弹系统效能进行建模。首先针对炮射导弹系统较为复杂的特点,将总炮射导弹系统分为四个分功能系统,通过系统正常与故障状态建立了可用性模型;其次构建了系统状态转移路径,将炮射导弹系统可信性分为发射可靠性、飞行可靠性与作用可靠性,依次建立可信性模型;接着针对ADC方法的不足,对系统固有能力进行拓展,增加了导弹质心参数、导弹射程参数、导弹制导控制力矩参数和导弹破甲深度参数指标,建立固有能力模型,改进了以往效能评估的局限性;最后确定炮射导弹系统效能;通过实验算例的验证表明了该方法模型可用、适用、准确。     

基于ADC模型的侦察卫星效能评估研究

以美国工业界武器系统效能咨询委员(WSEIAC)提出的ADC模型为基础,对侦察卫星系统自身特点进行分析,建立了系统ADC效能评估模型,并以此为基础,针对系统效能评估ADC模型不能反映侦察卫星侦察过程动态变化的问题,给出了解决方案,建立了动态ADC模型.在动态模型中,引入侦察卫星老化的问题并进行分析,应用泊松过程对卫星老化率进行了建模,给出了卫星老化率模型.     

基于UML和ADC法的舰载反舰导弹武器系统效能评估

为客观、正确地评价舰载反舰导弹武器系统效能的优劣,在参与系统效能分析的各个基本对象较为完备的情况下,借助于UML统一建模语言对系统进行可视化建模;在ADC法基础上,建立了一种着眼系统作战使用的定量评估舰载反舰导弹武器系统效能的方法,得出了系统效能评估模型,可较好地反映出舰载反舰导弹武器系统的实际效能。     

基于ADC的装甲装备体系作战效能评估研究

为有效评估装甲装备体系作战的综合效能,反映装甲装备作战特点,建立装甲装备体系作战效能评估的ADC模型,分析了基于层次分析法装甲装备体系能力指标权重的确定方法,提出了基于作战仿真系统获取评估模型数据的方法,并建立了基于仿真的评估系统体系结构。该方法不仅充分反映了平时装甲装备体系的战备效能,又能反映装甲装备体系在作战运用中的动态效能。     

武器装备系统效能评估方法研究综述

在简要介绍系统效能评估的基础上,重点综述了武器装备系统效能评估的ADC方法和SEA方法,内容包括方法的基本思想、应用难点、研究进展与应用情况,旨在为武器装备系统效能评估方法的研究与应用提供参考。     

关于地空导弹系统的效能评估

效能评估是地空导弹系统研发过程中的重要环节,直接关系到地空导弹系统成型度和实用性能。效能评估要根据地空导弹系统的相关数据指标来进行,建立科学合理的评估体系,运用先进的评估方法,是做好效能评估的关键。本文从地空导弹系统的效能入手,逐一分析效能评估的内涵及方法。     

可定制的效能评估系统研究与实现

效能评估技术在军事领域应用的扩大和深入,对效能评估系统的通用性提出了需求。为此本文通过分析总结典型效能评估方法,并结合效能评估特点,设计了一套可定制的效能评估系统。该系统基于MDA方法,运用了模型组合技术和插件式开发技术,可满足不同应用系统的效能评估需求,极大的提高了效能评估系统的开发效率。最后将该系统进行了代码实现,并通过具体应用证明了系统具有通用性、灵活性和扩展性。     

Scheduling preventive maintenance for oil pumps using generalized proportional intensities models

Percy and Alkali presented generalizations of the proportional intensities model introduced by Cox. They identified several features of these models that are particularly relevant for modelling complex repairable systems subject to preventive maintenance (PM). These include the baseline intensity, scaling factors and explanatory variables. We investigate these aspects in detail and apply the models to five sets of reliability data collected from the main pumps at oil refineries. We use likelihood methods to estimate the model parameters and compare how well the models fit the data. Our analyses suggest that a log‐linear baseline intensity function performs well and that an exponential deterministic scaling function is useful for corrective maintenance. The inclusion of explanatory variables to represent the quality of last maintenance and time since last maintenance also proves to be beneficial. We develop algorithms for simulating the reliability behaviour of a complex repairable system into the future, in order to schedule appropriate maintenance activities, identifying special cases that simplify the algebra. Applying these methods to the oil pump data, we derive recommendations for PM plans and demonstrate that adopting this strategy can lead to substantial savings.     

Optimal imperfect preventive maintenance policies for a used system

In some practical situations, it may be more economical to work a used system than do a new one. From this viewpoint, this article considers three basic preventive maintenance (PM) policies for a used system: the system with initial variable damage Y ₀ begins to operate at time 0, and suffers damage due to shocks. It fails when the total damage exceeds a failure level K and corrective maintenance is made immediately. To prevent such failure, it undergoes PM at a planned time T, a shock number N and a damage level k, but maintenances are imperfect. However, failure rate of a used system maybe higher than that of a new one, so some maintenance is applied to the policies at each shock in the extended models. Using the theory of cumulative processes, expected cost rate models are obtained, optimal policies which minimise them are derived analytically and discussed numerically.     

An economic production quantity model for deteriorating items with preventive maintenance policy and random machine breakdown

In recent years, many researches on economic production quantity (EPQ) models with machine breakdown and preventive maintenance have been developed, but few of them have developed integrated models for deteriorating items. In this study, we develop EPQ models for deteriorating items with preventive maintenance, random machine breakdown and immediate corrective action. Corrective and preventive maintenance times are assumed to be stochastic and the unfulfilled demands are lost sales. Two EPQ models of uniform distribution and exponential distribution of corrective and maintenance times are developed. An example and sensitivity analysis is given to illustrate the models. For the exponential distribution model, it is shown that the corrective time parameter is one of the most sensitive parameters to the optimal total cost.     

A preventive maintenance policy for a standby system subject to internal failures and external shocks with loss of units

In many situations, serious damage and considerable financial losses are caused by non-repairable failures of a system. Redundant systems and maintenance policies are commonly employed to improve reliability. This paper is focused on the modelling of a complex cold standby system by analysing the effectiveness and costs of preventive maintenance, always in an algorithmic form. The online unit of the system is subject to wear failures and external shocks. The online unit can go through an indeterminate number of degradation levels before failure. This one is observed when inspections occur. Inspections are performed at random intervals, and when one takes place, the unit is taken to the preventive maintenance facility if it is necessary. The preventive maintenance time and cost is different depending on the degradation level observed. If only one unit is performing, a minimal maintenance policy is adopted in order to optimise system behaviour. Reliability measures such as the conditional probability of failure are worked out in a well-structured and algebraic form in transient and stationary regimes by using algorithmic methods. The stationary distribution is calculated using matrix analytic methods, and rewards are included in the model. An optimisation example shows the versatility of the model presented.     

基于灾变机制的预防性维护和生产调度集成优化方法

为了解决生产车间中多品种任务的生产调度与预防性维护集成优化问题,综合考虑其加工顺序、生产批量及预防性维护策略等要素,在订单充足的前提下,以总制造成本和加工时间最小化为联合优化目标,建立了生产调度与预防性维护集成优化模型。针对模型特点,在非支配排序遗传算法框架的基础上,基于灾变机制和荣誉空间,引入截断和拼接操作算子,提出一种变长度染色体单亲遗传算法对模型进行求解,并在不同参数条件和问题规模下,通过仿真实验验证了该算法解决复杂生产任务调度和预防性维护集成优化问题的有效性。     

Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network

The milage of asphalt pavement growth explosively around the world in the past decades resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an effective strategy in saving budget, keeping the pavement in good condition, and extending pavement life. A particle swarm optimization (PSO) algorithm enhanced gated recurrent unit (GRU) neural network is developed in this research to predict five pavement performance parameters. The model is trained based on a dataset containing seven-year distress measurement data in 100-m intervals, traffic load data, climatic records, and maintenance records of a chosen highway in China. The random forest (RF) algorithm is used to analyze the influence of the factors on pavement performances for different lanes. The result shows the PSO-GRU model could increase the prediction accuracy by 21% on average compared with traditional ANN and 17% on average compared with the AdaBoost model. The validation case study shows a significant consistency between the predicted pavement quality index and the whole-year measurement data with a 0.67 coefficient of determination. This study demonstrates the potential of using the PSO-GRU neural network to provide the most effective treatment at a given location on a highway.     

A model for preventive maintenance operations and forecasting

Equipment costs constitute the greatest majority of overall costs for semiconductor manufacturing. Therefore, maintaining high equipment availability has been regarded as one of the major goals in the industry. The ability to forecast correctly equipment preventive maintenance (PM) timing requirements not only can help optimizing equipment uptime but also minimizing negative impacts on manufacturing production efficiency. This research used grey theory and evaluation diagnosis to construct a PM forecasting model for prediction of PM timing of various machines. The results showed significant improvements of PM timing predictions compared to the existing method based on experience and an alternative method proposed by Li and Chang (Semiconductor Manufacturing Technology Workshop 2002: 10–11, pp. 275–277) for the same fab cases. Received: June 2005 / Accepted: December 2005     

A bivariate optimal imperfect preventive maintenance policy for a used system with two-type shocks

This article studies an optimal imperfect preventive maintenance policy based on a cumulative damage model for a used system with initial variable damage. The used system is subject to shocks occurring to a non-homogeneous Poisson process, and suffers one of two types of shocks with stochastic probability: type-I shock (minor) yields a random amount of additive damage of the system, or type-II shock (catastrophic) causes the system to fail. A bivariate preventive maintenance schedule (n, T) is presented in which the system undergoes preventive maintenance at a planned time T and the nth type-I shock, or corrective maintenance at any type-II shock and the total damage exceeds a threshold level, whichever occurs first. The optimal preventive maintenance schedule which minimizes the expected cost rate is derived analytically and discussed numerically.     

基于柔性维护的堕化单机系统调度研究

针对单机系统,在假设生产系统为堕化系统,且生产过程中作业的加工不可中断的情况下,对考虑柔性时间窗口[[u,v]]下进行长度为[w]的周期预防性维护的调度问题进行了研究。建立了综合考虑生产调度和设备维护的混合整数规划模型,并设计了一套基于贪婪的启发式算法对所研究问题进行优化求解。通过Cplex和启发式算法求解结果的对比证明了算法可以快速、有效地解决此类问题。