机械系统相关竞争失效的可靠性评估与维修策略研究

机械系统中存在相关竞争失效会影响系统的性能,为确保机械系统的正常运行,需要经常对其进行检测和维修。该文将提升机械系统运行可靠性作为目标,进行了机械系统相关竞争失效的可靠性评估与维修策略研究。通过建立随机过程、基于Copula函数构建机械系统相关性模型、基于信息准则进行模型择优并构建多元退化失效模型,对其进行可靠性评估。再结合机械系统状态转移与系统维修建模,提出相应的维修策略,以期为提高机械系统运行可靠性以及降低维修成本提供参考。     

非计划停机下考虑结构相关性的串并联系统机会维修决策研究北大核心

以非计划停机下的多状态串并联系统为研究对象,假设将部件恢复至不同工作状态的维修方式有多种,建立以给定的工作状态阈值为限制条件的系统可靠度函数;同时将部件间的结构关系转换为系统维修前的拆卸序列,引入拆卸成本和拆卸时长,建立考虑结构相关性的机会维修模型。将拆卸成本和拆卸时长分别作为维修费用和耗时的组成部分,在费用和耗时有限的情况下,以系统可靠度为目标函数,决策出系统各部件的维修方式。通过案例分析表明,提出的机会维修模型能够有效决策出非计划停机下的维修活动组合。通过对比分析结构相关性对维修效果的影响,说明考虑结构相关性的维修决策模型更贴近生产实际。     

非计划停机下考虑结构相关性的串并联系统机会维修决策研究

以非计划停机下的多状态串并联系统为研究对象,假设将部件恢复至不同工作状态的维修方式有多种,建立以给定的工作状态阈值为限制条件的系统可靠度函数;同时将部件间的结构关系转换为系统维修前的拆卸序列,引入拆卸成本和拆卸时长,建立考虑结构相关性的机会维修模型。将拆卸成本和拆卸时长分别作为维修费用和耗时的组成部分,在费用和耗时有限的情况下,以系统可靠度为目标函数,决策出系统各部件的维修方式。通过案例分析表明,提出的机会维修模型能够有效决策出非计划停机下的维修活动组合。通过对比分析结构相关性对维修效果的影响,说明考虑结构相关性的维修决策模型更贴近生产实际。     

汽车运输经济性分析实例

通过对钢厂特种车辆设备不同车型的运行状态和成本的分析,研究了车辆作业量和维修成本等关系,揭示车型在钢厂的适用性和可靠性.运用设备经济性运行的手段,建立分析标准对车辆进行综合评估,为钢厂特种设备的选型、维修模式、报废周期、标准成本等提供决策依据,以使特种车辆实现经济运行、价值效益最大.     

船舶机械设备常见故障及状态监测技术分析

船舶制造水平的提升促进了我国海运事业的发展,在船舶性能提升的同时,船舶机械的结构和运行工况也日趋复杂,为船舶机械维修工作带来了一定的挑战,所以要加强对船舶机械设备的故障检修和状态监测。该文首先对船舶机械设备的常见故障进行总结分析,然后说明了比较常用的状态监测技术,可有效降低船舶机械维修成本,提高船舶机械设备运行的可靠性,为促进我国海运事业的长久发展创造有利的条件。     

考虑产品合格率衰变的设备维修与生产决策北大核心CSSCI

研究了需求不确定条件下生产设备的维修计划与生产决策的联合优化问题。首先建立了k-out-of-n系统设备状态的多周期纯生过程模型,然后在产品需求随机、产品合格率随设备状态劣化而衰减的情况下,分析了企业的维修成本、启动成本、库存或缺货成本、生产成本、质量成本,得到了企业单位时间成本最低的决策模型,并给出了较为详细的求解方法,最后通过算例验证了本模型的鲁棒性和精度,分析了设备劣化强度与合格品率的衰减率对模型结果的影响。研究结果表明,本模型能够使制造商以较低的单位时间成本满足市场随机需求且鲁棒性良好,此外设备的劣化强度及合格品率的衰减率都会显著影响企业生产维修与生产决策,尤其是合格品率的衰减率,它会造成企业的生产浪费,大大提高了企业运行成本,因此企业应该以适当频率对设备进行更新换代。     

考虑产品合格率衰变的设备维修与生产决策

研究了需求不确定条件下生产设备的维修计划与生产决策的联合优化问题。首先建立了k-out-of-n系统设备状态的多周期纯生过程模型,然后在产品需求随机、产品合格率随设备状态劣化而衰减的情况下,分析了企业的维修成本、启动成本、库存或缺货成本、生产成本、质量成本,得到了企业单位时间成本最低的决策模型,并给出了较为详细的求解方法,最后通过算例验证了本模型的鲁棒性和精度,分析了设备劣化强度与合格品率的衰减率对模型结果的影响。研究结果表明,本模型能够使制造商以较低的单位时间成本满足市场随机需求且鲁棒性良好,此外设备的劣化强度及合格品率的衰减率都会显著影响企业生产维修与生产决策,尤其是合格品率的衰减率,它会造成企业的生产浪费,大大提高了企业运行成本,因此企业应该以适当频率对设备进行更新换代。     

不完美检测下的控制图与预防性维修联合设计

目前,复杂系统的修复和检测费用往往比较高,过程比较繁琐,且由于设备故障停机造成了大量损失。针对这一问题,提出一种通过系统产出间接地判断整个系统是否存在问题,并在此基础上进行预防性维修的决策方法。与传统检测的方法不同,利用控制图监控产品,充当设备的传感器,间接地判断生产产品的设备是否正常。根据不完美检测下的监控结果,进行适当的维修策略,基于更换过程理论,建立单位时间成本最小的目标函数,提出控制图与预防性维修的优化参数设置。通过遗传算法进行数值仿真,表明该模型能有效实现控制图与预防性维修的联合经济设计目标。研究对于生产系统节约成本、提高综合经济效益具有现实意义。     

考虑产品故障率的生产提前期的优化分析

研究了一个考虑产品故障率的排队系统,在该模型中假设发现产品有质量问题时立即进行维修,且维修时间服从负指数分布.利用该系统对单阶段的装配或加工过程进行建模.应用马尔可夫过程的状态空间方法和全概率分解技术,得到了该系统的稳态队长分布、平均等待时间和平均逗留时间.提出了一个多目标优化的问题,利用评价函数法对模型的参数进行优化,得到了产品在生产线上最短生产提前期、最少加工成本和最少维修成本的优化方案.     

可用度及动态维修成本下的维修决策模型

为了制定一个更具操作性、符合生产实际的设备维修计划,采用现代设备维修管理理论,考虑了预防维修成本的动态性,构建出一种以预防更新周期内单位时间总维修成本最小为优化目标,以设备可用度为约束条件的预防维修决策模型。通过引入算例,运用枚举法,借助Matlab仿真软件来验证模型的合理性,同时证明了企业可采用该种决策模型来实现其维修成本的最小化。     

Optimal preventive maintenance in a production inventory system

We consider a production inventory system that produces a single product type, and inventory is maintained according to an (S, s) policy. Exogenous demand for the product arrives according to a random process. Unsatisfied demands are not back ordered. Such a make-to-stock production inventory policy is found very commonly in discrete part manufacturing industry, e.g., automotive spare parts manufacturing. It is assumed that the demand arrival process is Poisson. Also, the unit production time, the time between failures, and the repair and maintenance times are assumed to have general probability distributions. We conjecture that, for any such system, the down time due to failures can be reduced through preventive maintenance resulting in possible increase in the system performance. We develop a mathematical model of the system, and derive expressions for several performance measures. One such measure (cost benefit) is used as the basis for optimal determination of the maintenance parameters. The model application is explained via detailed study of 21 variants of a numerical example problem. The optimal maintenance policies (obtained using a numerical search technique) vary widely depending on the problem parameters. Plots of the cost benefit versus the system characteristic parameters (such as, demand arrival rate, failure rate, production rate, etc.) reveal the parameter sensitivities. The results show that the actual values of the failure and maintenance costs, and their ratio are significant in determining the sensitivities of the system parameters.     

Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate

This article deals with the combined production and maintenance plans for a manufacturing system satisfying a random demand. We first establish an optimal production plan which minimises the average total inventory and production cost. Second, using this optimal production plan, and taking into account the deterioration of the machine according to its production rate, we derive an optimal maintenance schedule which minimises the maintenance cost. A numerical example illustrates the proposed approach, this analytical approach, based on a stochastic optimisation model and using the operational age concept, reveals the significant influence of the production rate on the deterioration of the manufacturing system and consequently on the integrated production/maintenance policy.     

Using in-line equipment condition and yield information for maintenance scheduling and dispatching in semiconductor wafer fabs

Yield is one of the most important measures of manufacturing performance in the semiconductor industry, and equipment condition plays a critical role in determining yield. Researchers and practitioners alike have traditionally treated the problems of equipment maintenance scheduling and production dispatching independently, ignoring how equipment condition may affect different product types or families in different ways. This paper addresses the problem of how to schedule maintenance and production for a multiple-product, multiple-stage production system. The problem is based on the situation found in semiconductor wafer fabrication where the equipment condition deteriorates over time, and this condition affects the yield of the production process. We extend a recently developed Markov decision process model of a single-stage system to account for the fact that semiconductor wafers have multiple layers and thus make repeated visits to each workstation. We then propose a methodology by which the single-stage results can be applied in a multi-stage system. Using a simulation model of a four-station wafer fab, we test the policies generated by the model against a variety of other maintenance and dispatching policy combinations. The results indicate that our method provides substantial improvements over traditional methods and performs better as the diversity of the product set increases. In the scenarios examined, the reward earned using the policies from the combined production and maintenance scheduling method was an average of more than 70° higher than the reward earned using other policy combinations such as a fixed-state maintenance policy and a first-come, first-serve dispatching policy.     

Joint optimisation of maintenance and production policies considering random demand and variable production rate

This paper deals with a randomly failing manufacturing system M1 which has to satisfy a random demand during a finite horizon given a required service level. To help meet this demand, subcontracting is used through another production system M2. M1 operates with a variable production rate and its failure rate depends on both time and the production rate. In these conditions, as a first step, we establish a preliminary production plan corresponding to a given service level. In a second stage, we integrate the effect of the machine degradation introducing a unitary degradation cost. The optimal production plan is then obtained by minimising the sum of the production, the inventory and the degradation costs. In the final stage, we propose another optimal plan combined with a preventive maintenance policy aiming at reducing the machine degradation while minimising the total cost including the production, inventory and maintenance costs.     

An inventory model with emergency orders under explicit energy cost considerations

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any one of the three key factors increase with respect to energy cost.     

带缓冲区的基于时间延迟的可修设备联合优化

考虑到设备停机时间较长对企业经济效益的影响,在设备间设置缓冲区;同时考虑到设备单目标维修决策模型的不足,以及故障次数表达的精确程度对决策的影响,借用了时间延迟理论,建立了可修复设备的联合决策模型。为了满足设备的生产要求,以及最优化缓冲区生产系统的费用水平,将分别以平均单位时间的总停机时间、平均单位时间的总费用作为目标函数,来进行研究。通过时间延迟理论,分析了故障形成的过程,并表达出故障次数。借用更新报酬定理,来表达两目标函数。由离散迭代算法,求解得到：最优的检查周期和额定库存分别为25 d、1 195件时,总费用率在停机时间率为0.199时最小为22 739.95元。另外,进行了敏感性分析来验证最优解,最后由求解结果来指导生产线维修管理。     

Periodic imperfect preventive maintenance with two categories of competing failure modes

A maintenance policy is studied for a system with two types of failure modes: maintainable and non-maintainable. The quality of maintenance actions is modelled by its effect on the system failure rate. Preventive maintenance actions restore the system to a condition between as good as new and as bad as immediately before the maintenance action. The model presented permits to study the equipment condition improvement (improvement factor) as a function of the time of the preventive maintenance action. The determination of the maintenance policy, which minimizes the cost rate for an infinite time span, is examined. Conditions are given under which a unique optimal policy exists.     

和声搜索算法优化快速消费品生产配送协调调度

研究具有生产准备环节的快速消费品生产配送问题,考虑工厂和配送中心的库存限制,工厂产能限制和劳动力限制,建立一个多周期、多工厂、多产品、多配送中心、多客户的混合整数线性规划模型,旨在最小化准备成本、生产成本、库存成本和配送成本。通过设计一种遗传和声搜索算法对模型进行求解。最后给出一个算例说明所提模型和算法的可行性和有效性。     

Quality issue in forecasting problem of production and maintenance policy for production unit

In this study, we consider an unreliable deteriorating production system that produces conforming and non-conforming products to satisfy a random demand under a given service level and during a finite horizon. The production system is subjected to a failure-prone machine. The quality of the produced products is affected by the machine deterioration since the rate of defectives increases as the deterioration increases. Preventive maintenance actions can be piloted on the production system to reduce the influence of deterioration and the defective rate. A joint control policy is based on a stochastic production and maintenance planning problem with goals to determine, firstly, the economic plan of production and secondly, the optimal maintenance strategy. The proposed jointly optimisation minimises the total cost of production, inventory, maintenance and defectives. A failure rate and quality relationship are defined to show the influence of the production rates variation on the failures rate as well as on the defective rate. A numerical example and an industrial case study are adopted to illustrate the proposed approach and a sensitivity analysis to validate the jointly optimisation.     

Joint production and shipment lot sizing for a delivery price-based production facility

A joint economic production quantity (EPQ) and delivery quantity model for a production system is investigated in this paper. More specifically, an EPQ policy is implemented in the production system, while a smaller shipping quantity is periodically dispatched to the customer. The production system is also responsible for the shipment cost, i.e. a delivery price-based procurement from the customer. The considered cost includes setup cost to launch the batch production, inventory carrying cost, and transportation cost, where the transportation cost is a function of the delivery quantity. A per unit time cost model is developed and analysed to determine the optimal production and delivery quantities. Under some mild conditions, it can be shown that the joint cost function is convex with respect to the production quantity; and the number of delivery is an integer in each replenishment cycle. Computational study has demonstrated the significant impact of the joint decision model on the operating cost. In particular, the reduction in total cost can be more than 15% when inventory carrying costs, and/or transportation costs, are high.