基于可靠度经济性约束的不完全维修周期研究

利用役龄回退因子对不完全维修前后系统性能的动态变化进行了描述。充分考虑到航空装备预防维修周期随维修次数的变化情况,建立了有限时间区间内基于可靠度、经济性约束的预防性维修周期优化模型。以故障时间符合威布尔分布的航空装备为例,研究装备效益、维修周期、可靠度随预防维修次数的变化率,可有效地帮助决策者针对系统采取合适的维修周期。     

基于不完全维护的劣化系统分阶段顺序预防维护策略

多数系统维护之后的状态介于"全新"和"如旧"之间.基于不完全维护理论,引入考虑设备工龄和维护成本的改善因子,结合维护活动的实际情况,提出分阶段顺序维护的概念.以设备整个维修周期内的成本率最低为优化目标,建立两种顺序预防维护模型,即一般顺序预防维护模型和分阶段顺序预防维护模型(模型Ⅰ和模型Ⅱ),得到系统最优维护时间间隔和在全寿命周期内执行预防维护的次数,通过数值验证对各自模型的特点和有效性进行讨论.结果表明,分阶段顺序预防维护模型在不明显增加系统维护成本的前提下,既可以很好地适应劣化系统失效率随工龄增加而增长的特点,又能够贴近维修生产实践,使其在寻求最优维护策略方面兼具更好的经济性和可行性.     

考虑维护维修活动的单机调度集成模型

针对单机调度过程中设备进行维护维修活动所产生的不可用时间约束问题,采用基于故障率阈值的视情预防维修和基于役龄的小修相结合的混合维修策略,并考虑到预防性维修产生的不可用时间与设备的退化程度和维修次数之间的关系,建立了以工作次序和预防维修的故障率阈值为决策变量、以加工作业的总期望完成时间最小为优化目标的集成模型。运用智能优化算法对问题进行优化求解,并将集成模型与前人的工作进行了比较。实验结果表明,该单机调度集成模型能够缩短加工作业的总期望完成时间,提高生产效率。     

基于改善因子的系统部件维修间隔优化方法

针对民用飞机系统部件维修间隔优化问题进行研究。为了描述维修活动对系统的影响,引入改善因子描述维修活动对工龄的恢复效应,在不完全维修模型的基础上,构建了以单位时间总维修成本最小为目标函数的周期性不完全预防维修优化模型,得到了不完全维修条件下最佳预防维修时间间隔和最佳维修次数。最后,采用航空公司的实例进行验证,表明模型是可行的。     

PGA算法优化的航空装备预防性维修综合决策研究

考虑预防性维修次数和周期对航空装备故障率的影响,将役龄回退因子和故障率递增因子引入到故障率函数中,以有限时间内装备的维修费用最小和平均可靠度最大为优化目标,建立了一种变周期预防性维修综合决策模型。采用改进的Pareto遗传算法(Pareto Genetic Algorithm,PGA)对模型进行优化求解。以某型航空发动机为例对模型进行了验证,得到了10组Pareto最优集,确定了最佳的预防性维修次数及周期。仿真结果表明该模型在保证有效可靠度的基础上大大减少了维修费用。     

多状态可修系统的最佳非完好预防维修策略

针对多状态可修系统提出了一种预防性维修策略。假定系统存在多种状态,当系统每次的工作状态处于较差工作状态时,对系统进行预防维修,该预防维修视为"非完好维修",当预防维修次数达到N时,无论在下一个工作周期系统是否仍处于较差工作状态,系统将不再进行此预防维修。运用极大似然估计来估算系统元件的状态概率,借用通用生成函数的方法来获取系统各状态的概率值,将系统的工作时间描述为随机递减的几何过程,预防维修的时间描述为随机递增的几何过程,建立最优预防维修策略的数学模型,在确保系统单位时间内期望效益最大的条件下,给出最佳的预防维修策略值N*。     

非齐次缺陷发生率情况下时间延迟模型比较研究

为解决缺陷发生服从非齐次泊松过程和故障停机时间较长的问题,提出应用时间延迟理论对预防维修基本模型进行修正,并对设备预防维修间隔期进行优化决策.阐述了时间延迟概念及其维修理论,建立了复杂系统的基本预防维修模型.在此基础上,考虑到设备的故障停机时间较长、缺陷的发生服从非齐次泊松过程情况下合理的维修间隔期会产生相应变化,分别建立了预防维修修正模型Ⅰ和修正模型Ⅱ.通过案例研究,比较了顶防维修基本模型与修正模型关于确定合理维修间隔期的不同结果.研究表明,修正模型计算出的维修间隔期具有更高的精度.     

寿命型设备的预防维修策略研究

为解决寿命型设备在基于可靠度的预防维修下的经济维修策略问题,提出一种基于可靠度和经济性求解维修周期和维修时间策略的方法.基于可靠性理论,用维修度(维修时间的函数)近似描述维修降低的不可靠度比例,以可靠度和维修度服从指数分布为例,引入失效率递增因子和维修率递减因子,建立了设备可靠度与维修周期、维修时间的函数方程.基于该方程,以设备使用寿命内总收益为决策目标.建立了寿命型设备的预防维修周期和维修时间的策略模型.经优化计算得到不同参数下的预防维修策略.结果表明,寿命型设备的预防维修周期和维修时间呈先增长后缩短的趋势,且维修成本越高,维修次数和时间均越少.     

动力设备的两种更新模型

大多数动力设备(电动机、内燃机、……)是可维修设备.其特点是平均输出功率、平均寿命随维修次数的增大而减小;平均维修时间、平均维修费用随维修次数的增大而增大.动力设备的最大修理次数和最佳更新周期的确定既有实际意义又有理论价值。本文对某矿近500台40千瓦运输电机的寿命数据、维修时间及维修费用数据进行了统计分析,提出了按故障次数(可换算成修理次数)K 来更新动力设备的最大 K 值更新模型和最佳 K 值更新模型.最后给出一个应用此两种模型的计算实例.     

可靠度约束下不完全预防性维护经济优化模型EI北大核心CSCD

为解决可修系统设备因维修计划不合理而导致故障频发和资源浪费的问题,在利用包含役龄回退因子和故障率递增因子的混合故障率函数描述设备劣化过程的基础上,建立有限时间内基于可靠度约束的单设备动态不完全预防性维护模型。以铁路某轨道电路设备为例进行维修优化仿真,在设备最低工作可靠度限制下,以更换周期内总维修费用率最小为优化目标,确定其最佳预防性维修次数与弹性维修周期,并对比了无可靠度约束等维修周期维护模型的优化结果。研究表明,本维护模型能够有效保证设备高可靠运用,更好地降低设备故障率、节省维修费用,为制定最佳维修策略提供参考依据。     

考虑机会维修的等风险预防性维修策略优化

研究了对流水线生产系统进行等风险预防性维修策略优化的问题。预防性维修计划中包括不完全预防性维修和预防性替换，突发故障用最小维修处理。为了减少预防性维修造成的停产损失，一些预防性维修活动将根据机会维修阈值归并在一起进行。采用遗传算法在满足系统可靠性的前提下，以最小化维修成本为目标优化预防性维修计划。首先建立预防性维修的优化模型；然后设计了模型求解的遗传算法；最后在Emplant仿真环境下，将算法求解的最优预防性维修计划应用到生产系统仿真模型中进行评价，并与传统的故障替换策略进行了比较。     

Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem

In recent years, decision makers give more importance to the maintenance function, viewing its substantial contribution to business productivity. However, most literature on scheduling studies does not take into account maintenance planning when implementing production schedules. The achievement of production plan without taking into account maintenance activities increases the probability of machine breakdowns, and inversely, considering maintenance actions in production planning elongates the achievement dates of orders and affects deadlines. In this paper, we propose a bi-objective model to deal with production scheduling and maintenance planning problems simultaneously. The performance criteria considered for production and maintenance are, respectively, the total tardiness and the unavailability of the production system. The start times of preventive maintenance actions and their number are not fixed in advance but considered, with the execution dates of production tasks, as decisions variables of the problem. The solution of the integrated model is based on multi-objective ant colony optimization approach. The proposed algorithm (Pareto ant colony optimization) is compared, on the basis of several metrics, with well-known multi-objective genetic algorithms, namely NSGA-II and SPEA 2, and a hybrid particle swarm optimization algorithm. Interesting results are obtained via empirical study.     

Metaheuristic-based inspection policy for a one-shot system with two types of units

In this paper, we address an inspection policy problem for a one-shot system with two types of units, namely, Type 1 units that fail at random times and Type 2 units that degrade with time. Interval availability and life cycle cost are used as optimization criteria and estimated by simulation. We determine inspection intervals, preventive replacement ages of Type 1 units, and preventive maintenance thresholds of Type 2 units that have minimal life cycle cost and satisfy the target interval availability during inspection periods. A simulation-based optimization procedure using a hybrid genetic algorithm is proposed to find near-optimal solutions. Numerical examples are studied to investigate the effects of model parameters on optimal solutions and compare the hybrid genetic algorithm with the general genetic algorithm.     

航空装备多部件预防性维修优化研究

应用延迟时间模型,描述航空装备多部件系统的故障过程,在维修事件分析的基础上,以费用率最小为优化目标,建立了多部件维修费用率模型,通过设计迭代算法对模型求解,实现了航空装备多部件预防性维修周期优化。     

考虑动态折旧成本的动车组部件预防性维修决策模型

为研究部件自身价值变化对其维修决策的影响,以动车组四级修时需更换的部件为研究对象,结合我国动车组现行的多级非完美维修方式,建立了一种考虑动态折旧成本的动车组部件两级非完美预防性维修优化模型,该模型以动车组部件的等效役龄计算其相应的折旧成本。进一步研究了部件折旧成本与故障率变化相关联的维修决策方法。研究结果表明,与不考虑部件折旧成本的维修模型相比,该模型兼顾了部件的生产运行和维修活动对维修决策的影响,能够更好地平衡预防性维修成本和故障维修成本,使模型更具维修经济性。     

电磁轴承柔性转子系统动态响应分析

给出了基于多输入多输出(M IMO)系统建模方法的电磁轴承柔性转子系统动态响应优化算法,通过数值仿真实例搜索得到系统最佳工作点。对电磁轴承柔性转子系统动态响应为目标函数的寻优规划的凸凹性进行了判定,对寻优结果作了鲁棒性分析,据此对寻优结果的可靠性给出了定量结论。计算与分析结果表明:电磁轴承柔性转子系统动态响应寻优规划为非凸规划,通过本文提出的优化仿真方法得到的系统最佳工作点为局部最优解且逼近全局最优解。     

Reliability design of preventive maintenance scheduling for cumulative fatigue damage

As the cumulative running times of a locomotive truck increases, degradation such as fatigue, wear, and deterioration occur. Particularly the container train and uncovered freight train, their maintenance cost during their lifetime is three times more than the manufacturing cost. Generally, for the freight train, corrective maintenance to repair a bad part after a breakdown is not adapted; however, preventive maintenance that fixes a bad part before a breakdown is. Therefore, it is important and necessary to establish a system of optimal preventive maintenance and exact maintenance period. This study attempts to propose a preventive maintenance procedure that predicts a repair period using reliability function and instantaneous failure rate based on fatigue test and load history data. Additionally, this method is applied to the end beam of an uncovered freight train, which is the brake part, and its usefulness is examined and analyzed. This paper was recommended for publication in revised form by Associate Editor Chongdu Cho Seok-Heum Baek received a B.S. and M.S. degree in Mechanical Engineering from the Dong-A University in 2001 and 2003, respectively. He is currently a Ph.D. student at the School of Mechanical Engineering at Dong-A University in Busan, Korea. Student Baek works on ceramic composite design and robust and reliability-based design, and his research interests are in the areas of trade-off analysis in multicriteria optimization, design under uncertainty with incomplete information, and probabilistic design optimization. Seok-Swoo Cho received a B.S. degree in Mechanical Engineering from Dong-A University in 1991. He then went on to receive his M.S. from Dong-A University in 1993 and Ph.D. degree from Dong-A University in 1997. Dr. Cho is currently a Professor at the Vehicle Engineering at Kangwon National University in Kangwon-do, Korea. Dr. Cho works on crack growth modeling and composite design and optimization, and his research interests are in the areas of structural optimization and inverse and identification problems, and X-ray diffraction, brittle collapse and crack propagation, fatigue fracture phenomena. Hyun-Su Kim received a B.S. degree in Mechanical Engineering from Seoul National University in 1978. He then went on to receive his M.S. from KAIST in 1980 and Ph.D. degree from University of Iowa in 1989. Dr. Kim is currently a Professor at the Mechanical Engineering at Dong-A University in Busan, Korea. His research interests are in the area of high temperature creep fatigue, bio-engineering, design using the finite element method, optimization, and MEMS. Won-Sik Joo received a B.S. degree in Mechanical Engineering from Dong-A University in 1968. He then went on to receive his M.S. from Dong-A University in 1978 and Ph.D. degree from Kookmin University in 1988. Dr. Joo is currently a Professor at the Mechanical Engineering at Dong-A University in Busan, Korea. His research interests are in the area of creep and fatigue in high temperature alloy, fatigue design, and strength evaluation.     

Scheduling sequence-dependent setup time job shops with preventive maintenance

Although many researchers have proposed different techniques to integrate production scheduling and preventive maintenance, these techniques have some drawbacks. For example, some of them are so intricate that one cannot easily implement them, or some strongly exploit specific features of the original studied problem that one cannot apply them to other problems. We hereby propose two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems. This paper investigates job shop scheduling with sequence-dependent setup times and preventive maintenance policies. The optimization criterion is to minimize makespan. Four metaheuristics based on simulated annealing and genetic algorithms as well as adaptations of two metaheuristics in the literature are employed to solve the problem. The performances of the proposed algorithms are evaluated by comparing their solutions through two benchmarks based on Taillard’s instances.