基于服务性能合同模式下单部件系统视情维修策略研究

针对供应商系统维修的低效率以及维修成本参数较难获得的问题,提出了基于服务性能合同模式(PBC)下的单部件系统最优视情维修策略模型。首先,基于Gamma分布,描述单部件系统连续递增的退化过程,依据系统实时检测状态与预防维修阈值、故障阈值之间的关系,实施不同的维修策略;其次,分析单位更新周期内的检测次数和使故障设备恢复如新的维修方式,以供应商利润率最大化为目标函数,以最佳维修阈值与检测间隔时间为决策变量,建立以利润为中心的视情维修优化模型;最后,利用改进灰狼算法求解数学模型,通过算例验证所提出模型的有效性,并进行了各维修费用参数对目标函数以及最优维修策略的灵敏度分析。     

可用度约束条件下的设备不完美预防维修策略

针对2M1B生产系统,基于设备实际维修情况,提出了故障设备不完美预防维修策略。首先,考虑设备随时间不断劣化的情况,基于准更新过程,建立了生产周期内设备随机故障次数的表达式,计算了设备维修总费用。其次,通过分析缓冲区内库存的变化轨迹,以生产周期内设备随机故障次数为基础,计算了缓冲区库存费用,综合设备维修费用和缓冲区库存费用,构建了周期内生产总成本模型。以满足系统最低要求的可用度水平为约束条件,以预防维修周期和缓冲区库存量为决策变量,以生产周期内单位时间总成本为目标函数,通过离散迭代算法获得最优预防维修周期和此周期下的最佳缓冲区库存量。最后,通过数值分析验证了模型的有效性,为制定最佳维修策略提供了有效依据。     

基于仿真的生产设备预防性维修周期研究

为了对生产系统中的设备故障加以有效的控制，从而减少设备故障的发生，文章对生产设备的预防性维修周期问题进行了研究。通过建立生产设备预防性维修的费用模型，利用仿真的方法对不同维修策略的预防性维修周期进行优化选择。该模型综合考虑了修复性维修成本、预防性维修成本和生产损失成本。最后以故障分布形式为威布尔分布的设备为例进行仿真实验，得出设备最优维修周期结果，并阐述了经济原则对预防性维修周期的影响。     

基于大数据的电力企业资产组维修策略优化研究

汇总过去若干年的电力设备故障数据,运用大数据分析方法,把故障预测技术引入到预防性维修的实践中,提出一种基于大数据的预防性维修策略。首先,根据由状态检测信息得到剩余寿命的预测结果,以预防性维修时的剩余寿命为阀值制定预防性维修策略。然后,根据更新过程理论,建立以电力设备的预防性维修阀值和预测间隔期为优化变量,综合考虑电力设备维修成本、客户满意度、电量销售、停电损失、维修时机选择等约束条件呢,以电力设备平均维修费用最小和电力设备可用度最大为优化目标的预防性维修优化模型。采用人群搜索算法进行优化求解,得到系统最佳的预防性维修阀值和维修预测间隔期。最后,通过引入算例,对所建模型优化仿真求解,得到电力设备最佳的预测周期,在保证电力设备可用度的同时,使电力设备的平均维修费用最小,验证了所建模型的可行性和有效性,从而提高电力企业的整体效益。     

冷/温混合贮备系统最优切换及视情维修决策

针对周期性切换冷/温混合贮备系统,研究其最优切换以及视情维修决策,在系统劣化建模的基础上,分析系统结构和切换式运行维修特性,制定基于周期切换和检测的离线视情维修策略.首先,通过分析系统运行与备用设备交替使用、维修过程中的状态转移特性,推导各检测周期时刻系统状态概率分布模型以及各维修活动的概率;然后,以系统有限时间范围内平均费用率最小为目标建立解析优化模型,以决策最优切换周期和维护阈值,并采用遗传算法对模型进行求解;最后,以汽轮发电机定子冷却水泵系统为对象验证策略和模型的正确性和有效性,并对参数进行灵敏度分析.实验结果表明,所提出离线视情维修策略能够有效地降低系统的维修成本.     

基于健康管理的有源相控阵雷达威力性能评估方法

针对有源相控阵雷达一类的复杂电子系统,设备的维修是产品全寿命周期内最核心的工作之一。装备的过维修或欠维修,都将对装备的使用造成影响,同时提高了保障费用。本文通过有源相控阵雷达的健康管理系统的参数获取、分析和知识挖掘,提供了一种基于健康管理的雷达威力性能的评估方法,进而提升雷达全寿命周期保障的自动化、智能化水平,达到降低全寿命周期费用,提高装备作战效能的目标。     

基于二阶段时间延迟的多产品生产系统生产与维修联合优化研究

针对多产品生产部件串联系统的生产和维修问题进行了研究,提出了基于二阶段时间延迟的联合优化模型。首先,基于生产周期分段理论,将整个周期等分成若干单位时间段,生产与维修共用每段时间,且若干时间段后采取一次预防维修。其次,考虑生产系统的实际生产时间、可用生产时间和维修耗费时间,建立了生产计划与维修计划总成本模型。其中,维修计划考虑缺陷和故障维修费用、维修检查费用,以及非正常状态下设备运行可能产生的不合格产品损失费用;生产计划考虑生产成本、库存成本、延期未交货成本和维修停机后恢复生产的设备启动成本。最后,通过算例分析,计算最优预防维修周期和各单位时间段各产品产量,验证了模型的有效性。     

基于性能合同的多部件系统维修策略模型优化

针对多部件串联系统维修效率低下与供应商所获利润少的问题，考虑各部件间的经济相关性，提出基于性能合同的多部件系统的维修策略模型。首先，采用韦伯（Weibull）分布描述系统各部件的使用寿命规律，并通过判断各部件使用度与预防性维修阈值和机会维修阈值的关系来实施不同的维修策略；其次，计算单位更新周期内的各维修活动概率和对应维修次数，以供应商利润最大化为目标，以预防性维修阈值和机会维修阈值为决策变量，建立基于性能合同的多部件系统的维修策略模型；最后，利用灰狼优化（GWO）算法求解该模型。通过算例分析得出与遗传（GA）算法、粒子群优化（PSO）算法相比，灰狼优化算法在准确率上分别提高了22.6%和7.6%；基于线性收益函数，所提绩效模型的利润率高达25.3%，相较于传统成本模型提高了5.2%。基于性能合同的多部件系统维修策略优化模型和算法可以有效解决供应商维修质量与效率低下问题，为供应商和运营商共同制定维修合同提供依据。     

考虑劣化状态的单机调度与维修决策集成模型

大多数研究单机调度与维修决策集成问题的文献采用基于役龄的维修策略. 然而, 设备劣化状态与加工对象、加工环境和加工时间等诸多因素相关. 鉴于此, 针对设备状态可检测的系统, 采用非完美预防性视情维修、小修与故障更换相结合的维修策略, 建立一种以加工作业次序和预防维修阈值为决策变量, 加工作业的总加权期望完成时间最小为优化目标的随机期望值集成模型. 实验结果表明, 所提出的模型能更有效地避免过维修或欠维修, 并且能够降低生产持有成本.

     

风电机组齿轮箱动态周期不完全维修优化

齿轮箱作为风电机组的关键设备,造价高昂,长期工作在变工况条件下,一旦故障,会造成较大的停机损失和维修成本。对齿轮箱进行维修策略优化,可以有效降低维修费用、提高机组可用度。针对该需求,结合齿轮箱实际运行维修工况,引入役龄修正因子和失效率增长因子,对传统失效率模型进行修正。同时,考虑可靠度和经济性的相互影响,以齿轮箱全寿命周期总维修成本最小为优化目标,建立了风电机组齿轮箱动态周期预防性不完全维修优化模型。将该模型用于某风电场1.5 MW机组齿轮箱的维修决策,分别计算了只考虑可靠度限制和兼顾可靠度限制和经济性的维修周期和维修成本。计算结果显示,兼顾可靠度限制和经济性条件下的优化模型可优化维修周期、降低维修成本,有效提升风电场的经济效益。     

Tool maintenance optimization for multi-station machining systems with economic consideration of quality loss and obsolescence

Tools used in a machining process are vulnerable to frequent wear-outs and failures during their useful life. Maintenance is thus considered essential under such conditions. Additionally, it is widely recognized that the maintenance of manufacturing equipments and the quality of manufactured product are highly interrelated. However, few detailed study has been found in the literature dealing with the effects of maintenance policies on the operational performance of such a system, especially the long-term average cost. The need for a method to determine the optimal tool maintenance policy has become increasingly important. Since the multiple tools in a multi-station machining system generally have significant interactive impacts on the product quality loss, the optimal multi-component maintenance models for several policies are investigated to address the interdependence among these tools. Three distinctive multi-component maintenance policies, i.e., age replacement, block replacement, and block replacement with minimal repair, are identified and analyzed. The proposed approach focuses on these maintenance policies with consideration of both component catastrophic failures, and the interdependence of component degradations on the product quality loss as well as the obsolescence cost. The effects of various maintenance policies on the system performance are simulated, and they are used to determine the best policy for a given system. An illustrative example is used to demonstrate effectiveness and applicability of the proposed approach. The results presented a comparative analysis of specified maintenance policies with respect to the total maintenance cost with consideration of the product quality loss and the obsolescence cost.     

Optimal inspection and economical production quantity strategy for an imperfect production process

This paper considers a problem relating to the integration of production, maintenance, inspection and inventory. The optimal inspection interval, inspection frequency and production quantity were studied, assuming an imperfect production process and a maintenance policy that permits minimal repair, shortages and inspection errors. When the process is in the in-control state, preventive maintenance is performed. If the process is in the out-of-control state, there are two possibilities. The first is a relatively mild production process shift, where minimal repair can bring the process back into the in-control state. The second possibility is a more serious shift, where minimal repair cannot bring the process back into the in-control state. In this case, it will be necessary to halt production and perform repair or replacement. It is accepted that, when a production process is in the out-of-control state, an amount of defective goods will be produced. Within the context of a deteriorating production process system, the advantages of preventive maintenance were explored, using numerical analysis to analyse the impact of inspection errors, permitted shortage conditions and minimal repair on cost.     

Optimisation of PM scheduling for multi-component systems – a simulated annealing approach

Proper planning of preventive maintenance (PM) is crucial in many industries such as oil transmission pipelines, automotive and food industries. A critical decision in the PM plans is to determine frequencies and types of maintenance actions in order to achieve a certain level of system availability with a minimum total cost. In this paper, we consider the problem of obtaining availability-based non-periodic optimal PM planning for systems with deteriorating components. The objective is to sustain a certain level of availability with the minimal total maintenance-related costs. In the proposed approach, the planning horizon is divided into some inspection periods of equal intervals. For any given interval, a decision must be made to perform one of the three actions on each component; inspection, preventive repair and preventive replacement. Any of these activities has different effects on the reliability of the components and the corresponding distinct costs based on the required recourses. The cost function includes the cost for repair, replacement, system downtime and random failures. System availability and PM resources are the main constraints considered. Since the proposed model is combinatorial in nature involving non-linear decision variables, a simulated annealing algorithm is employed to provide good solutions within a reasonable time.     

复杂可修装备维修策略优化研究综述

装备维修是保持武器装备作战效能的重要工作,制定科学合理的维修策略是完成装备维修工作的前期。针对复杂可修装备维修策略制定问题,分析和总结了维修策略的发展现状,重点从维修阈值、检测间隔期和决策目标优化三个方面对维修决策近年来的研究成果进行了综述,并结合已有的研究基础,对维修策略优化的未来发展趋势进行了展望。     

Sensitivity analysis and comparison of algorithms in preventive maintenance and replacement scheduling optimization models

In this research, new optimization models are developed to determine the optimal preventive maintenance and replacement schedules in repairable and maintainable systems. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability over the planning horizon. Experimental results of a sensitivity analysis on the optimization models are presented and evaluated. These experiments investigate the effect of the parameters on the structure of optimal preventive maintenance and replacement schedules in multi-component systems. Two factorial design experiments based on the cost associated with maintenance and replacement activities and reliability characteristic parameters are constructed and analyzed. In addition, a comprehensive experiment is designed to analyze and compare the efficiency and accuracy of the exact and metaheuristic algorithms.     

A condition-based maintenance strategy for heterogeneous populations

This paper develops a maintenance strategy, called inspection–replacement policy, to cope with heterogeneous populations. Burn-in is the procedure by which most of the defective products in a heterogeneous population can be identified and removed prior to being placed in service. However, modern manufacturing is so well developed that a defective product is able to function for a long period of time even under aggravated operational conditions. Instead of weeding defective products out via costly burn-in tests, use can be made of them in field operation where maintaining actions will be performed to prevent early in-use failures. The inspection–replacement policy consists of an inspection, conducted in an early stage with the purpose of identifying and replacing defective products, and a preventive replacement, carried out at a later stage to prevent wear-out failures. The preventive-replacement time is dynamically determined, depending on the information obtained by the inspection. The inspection–replacement policy is compared with a joint burn-in and age-based-replacement policy to show its practicability and competence.     

A condition-based maintenance policy for a production system under excessive environmental degradation

In this paper a condition-based maintenance model is proposed for a single-unit system of production of goods and services. The system is subject to random deterioration which impacts not only the product quality but also the environment. We assume that the environment degrades whenever a specific level of system deterioration is reached. The proposed maintenance model aims to assess the degradation in such a way to reduce the deterioration of the environment. To control this deterioration, inspections are performed and after which the system is preventively replaced or left as it is. Preventive replacement occurs whenever the level of the system degradation reaches a specific level threshold. The objective is to determine optimal inspection dates which minimize the average total cost per unit of time in the infinite horizon. Cost function is composed of inspection and maintenance costs in addition to a penalty cost due to environmental deterioration. The maintenance optimization model is formally derived. On the basis of Nelder–Mead method, inspection dates as optimal solutions are computed. A numerical example is provided to illustrate the proposed maintenance model.     

Optimal single-replacement for repairable products with different failure rate under a finite planning horizon

This paper provides a replacement policy for repairable products with free-repair warranty (FRW) under a finite planning horizon from the consumer's viewpoint. Assume that the product is replaced once within a finite planning horizon, and the failure rate of the second product is lower than the failure rate of the first product. Within FRW, the failed product is corrected by minimal repair without any cost to the consumers. After FRW, the failed product is repaired with a fixed repair cost to the consumers. However, each failure incurs a fixed downtime cost to the consumers over a finite planning horizon. In this paper, we derive the three models of the expected total disbursement cost within a finite planning horizon and some properties of the optimal replacement policy under some reasonable conditions are obtained. Finally, numerical examples are given to illustrate the features of the optimal replacement policy under various maintenance costs.     

On problems of multicomponent system maintenance modelling

We present an overview of some recent developments in the area of mathematical modeling of maintenance decisions for multi-unit systems. The emphasis is on three main groups of multicomponent maintenance optimization models： the block replacement models, group maintenance models, and opportunistic maintenance models. Moreover, an example of a two-unit system maintenance process is provided in order to compare various maintenance policies.     

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.