Maintenance strategy for leased equipment

In case of leasing, the user rents equipment for a predetermined time. During this period, all maintenance actions are performed by the lessor. The aim of this research paper consists in determining an optimal maintenance policy for ensuring a minimum reliability, required by the customer. Two strategies are proposed: the first consists in performing preventive actions whenever the system reliability reaches a predefined reliability threshold. These actions are characterized by a reduction of the system age. The objective is therefore to determine the effectiveness factor of the optimal maintenance minimizing maintenance costs. For the second strategy, “improving” actions replace corrective actions during an interval to be determined to minimize maintenance costs. The first strategy will be solved using a numerical procedure and the second strategy uses an algorithm of discrete event simulation.     

Optimal maintenance policy incorporating system level and unit level for mechanical systems

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.     

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。     

带缓冲区且产品保修的设备预防性维护建模

论文考虑的是一个完美的生产系统,即生产系统所生产的产品均没有质量问题。为提高设备的可靠性,调整市场需求,提出带缓冲区的设备预防性维护优化建模方法。已经出售的产品,顾客在使用过程中可能会出现故障,基于这个情况,在保修期内会予以免费维修。找出生产系统的运行时间(T)和缓冲区库存量(S),以此为决策变量建立期望成本模型。在此基础上,构造求解最优策略的算法。通过数值算例演示了该模型,通过灵敏度分析确定了最优策略和相关参数的关系。     

Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.     

Joint determination of lease period and preventive maintenance policy for leased equipment with residual value

This paper investigates the influence of the length of the lease period on the maintenance policy for leased equipment with residual value. The length of the lease period increases, however, the lessor’s income increases, and the maintenance cost of the equipment rises as well. Therefore, the lease payment and maintenance service of the equipment are crucial items in the lease contract for the lessor’s profit. If the equipment breaks down within the lease period, minimal repairs will be performed on the equipment and the lessor may incur a penalty cost if the repair time exceeds a pre-specified tolerable time. The imperfect preventive maintenance (PM) actions are carried out when the age of the equipment reaches a controlled-limit during the lease period. Under this maintenance scheme, the mathematical model of profit is constructed and then the optimal maintenance policy and the length of the lease period are obtained such that the expected total profit is maximized. Finally, numerical examples are given to illustrate the effects of the optimal length of the lease period and the maintenance policy for profit model.     

Preventive maintenance scheduling for repairable system with deterioration

Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system’s reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system’s reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system’s failure rate function and operational cost are both considered by the effect of system’s corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.     

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。     

Optimally maintaining a multi-state system with limited imperfect preventive repairs

In this paper, the optimal maintenance policy for a multi-state system with no observation is considered. Different from most existing works, only a limited number of imperfect preventive maintenance actions can be performed between two successive replacements. Assume that the system's deterioration state cannot be observed during its operation expected after each replacement, and it evolves as a discrete-time Markov chain with a finite state space. After choosing the information state as state variable, the problem is then formulated as a Markov decision process over the infinite time horizon. In order to increase the computational efficiency, several key structural properties are developed by minimising the total expected cost per unit time. The existence of the optimal threshold-type maintenance policy is proved and the monotonicity of the threshold is obtained. Finally, a numerical example is given to illustrate the optimal policy.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Two-stage Maintenance of a Production System with Exponentially Distributed On- and Off-periods

We consider an on–off production system which is subject to failure during on-periods. In case of a failure, the production system is maintained correctively. In addition, preventive maintenance is carried out to prevent failures. The costs of both preventive and corrective maintenance are modelled in terms of the net down-time of the production system, that is, the time that the system is not available when needed for production. Since preventive maintenance can be planned during off-periods, there is a perspective of significant savings if some freedom is built in concerning the starting time of preventive maintenance. Therefore, a two-stage maintenance policy is considered, which—in a first stage—provides the maintenance manager with a finite interval during which preventive maintenance must be carried out, and—in a second stage—determines the optimal starting time for preventive maintenance within this interval. Computational results offer useful insights, and indicate that significant savings can be achieved in comparison with a classical age maintenance policy.     

Preventive maintenance scheduling for a multi-component system with non-negligible replacement time

When maintenance models are developed for complex continuous operating systems, with large production losses, it is important to take into account the maintenance duration, in order to provide an effective maintenance program. In this work, a preventive maintenance model is presented in order to coordinate the component replacements in a multi-component system. The model is based on the partial periodic renewal policy. At each partial renewal, a group of components to be replaced is defined. When all components are replaced simultaneously, we consider that the system undergoes an overhaul which resets it to the new state. Moreover, the model takes into account for non-negligible replacement times, where different assumptions are considered. The relevant effect of maintenance times on the optimal policy is clearly shown by the numerical results, provided by the application in an oil refinery.     

基于PI的转动设备预维护系统的设计

为方便工厂转动设备的维护,该文设计了一套转动设备预维护系统,可用于工厂转动设备可靠性预计、维护预警,为工厂维护计划的制定提供参考。设备预维护系统充分利用工厂实时数据库系统(PI)提供的数据,计算设备的平均无故障时间(MTBF),并在设备运行时间达到或超过预设上限时警示该设备需要维护。通过某化工厂的运行实践后证明,此系统明显提高了维护人员的工作效率。     

考虑中间库存缓冲区的设备不完美预防维修策略研究

针对带有库存缓冲区的设备维修问题,提出了随机故障设备的不完美预防维修策略。首先,考虑设备随机故障率随故障次数的增多而变大的情况,基于准更新过程,建立随机故障设备的故障次数表达式。其次,结合设备故障次数表达式,综合考虑维修成本和库存缓冲成本的基础上,构建了设备生产成本模型,以缓冲库存量和预防维修周期为自变量,以生产成本为目标函数。获得设备的最优维修策略和最佳库存缓冲量。最后,通过算例分析验证了模型的有效性,为生产线设计的可行性提供了依据。     

考虑维修优先权的多状态温贮备系统可靠性模型

针对工程应用中装备维修和保养两类活动具备不同优先级这一问题,考虑单一维修台可进行维修和保养两类工作,其中维修具有优先权.以多状态温贮备系统为研究对象,采用可近似拟合任意分布的Phase-type(PH)分布构建一种描述能力更强的系统可靠性模型,得出系统稳态可用度、系统故障率、平均故障间隔时间等一系列可靠性指标的解析表达式.最后利用算例验证PH分布的适用性,演示系统可靠性函数随时间的变化趋势,并讨论维修台工作速率对系统故障率、稳态可用度、维修台忙期稳态概率和平均故障间隔时间的影响.算例结果表明,所提模型具有较广泛的应用价值.     

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。     

需求随机波动下多设备批量生产系统的视情维护策略研究

针对需求随机波动情况下多设备批量生产系统的设备维护问题,提出了一种基于滚动生产计划和设备退化状况的视情维护策略。首先,通过滚动时域规划方法预测不同产品的随机需求并在此基础上以总生产成本最小确定滚动生产计划。其次,在每一滚动生产周期开始前检测系统中各设备的退化水平,利用Gamma过程描述退化增量,以最小维护成本率确定当前退化状态下各设备的最佳维护时间,同时为避免生产过程中断利用提前延后维护策略对预防维护进行动态调整。在系统层,利用生产转换时机对需要维护的组件进行组合维护。再次,引入时间约束和服务水平约束,建立批量生产与视情维护的联合优化模型,以总成本最小为目标,确定实际生产计划和维护计划。最后,通过算例以整个生产计划期内的总成本和故障次数为度量验证了所提出的多设备批量生产系统视情维护策略的有效性。     

设备预防维修管理系统研究

预防维修是将来设备最主要的维修方式。本文对开发设备预防维修管理系统的关键环节进行了阐述。建立了失效率分布的数学模型，并以二参数威布尔分布为例对设备故障时间和最佳预防维修周期进行了求解。     

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.