劣化系统的最佳检测与维修周期分析

提出了一类基于半马氏决策过程的劣化失效系统检测与维修优化模型.将位相型(PH)分布引入模型后,决策过程的状态空间发生变化,为了获得适用于原有模型假设的检测与维修优化策略,提出了一种改进的值迭代算法.运用该算法,求得了使系统长期运行平均费用率最低的预防性维修阈值与检测间隔分布.最后,通过具体算例验证了模型与迭代算法的可行性.     

基于相关性的多部件系统机会成组维修优化

针对传统的维修相关性分类方法没有全面考虑维修相关性影响的问题,提出从时间相关性、故障相关性、结构相关性和功能相关性四个方面描述多部件系统的维修相关性的方法,分析了各类相关性对机会成组维修的影响。以部件可用代价最小化为目标,建立多部件系统机会成组策略的优化模型,通过采用生物地理学优化算法求解,确定系统内各部件与其维修相关部件的最佳机会维修阈值集。通过算例分析了某船用燃油喷射系统中输油阀与柱塞和针阀两维修相关部件进行机会维修的成组时机,表明该模型为多部件系统的维修成组提供了切实可行的方法。     

考虑非完美维修的多部件系统劣化状态空间划分建模

为了进一步深入研究考虑非完美维修效果的建模方法,利用劣化状态空间划分思想对考虑非完美维修的多部件系统视情机会维修策略进行了统一建模。针对多部件系统,考虑非完美维修效果,制定了基于周期检测的视情机会维修策略,建立了联合劣化状态空间划分模型,给出了所有维修需求组合及其概率计算通式,推导了联合稳态概率密度函数。通过数值实验验证了上述模型及联合稳态概率密度函数求解的正确性,同时定性分析了该建模方法对研究相同多部件系统维修优化建模的有效性,为后续的维修优化研究奠定了基础。     

基于视情维修的联合优化策略研究

对于退化型失效的数控机床关键功能部件,其可靠性数据具有小样本的特点。融合相似型号数控设备中部件的故障数据,构建同类部件的寿命分布函数;针对某一具体部件进行定期的状态监测,并进行剩余有效寿命预测。联合视情维修与计划性维修,构建基于维修费用最小的维修优化模型。最后,基于蒙特卡洛方法进行维修费用与维修周期的优化求解。实验采用某电主轴/刀柄结合面进行了验证,实验表明此方法能为维修费用预算及确定维修周期提供技术支持。     

基于比例风险模型的装备机会维修阈值优化

对包含多组多部件装备的复杂系统进行维修时,通常对装备之间的相关性考虑不足。针对这一问题,提出一种结合状态维修与机会维修的维修策略。采用比例风险模型对部件的剩余寿命进行预测,将预测结果作为制定维修策略的依据,分别在进行机会维修时采用完全维修、不完全维修和混合维修3种维修策略,并将平均维修费用最少作为决策目标,优化了3种维修策略的机会维修阈值,计算了相应的最低维修费用并进行了比较。以某包含多组多部件装备的复杂系统维修问题为例,验证了所提方法的有效性和实用性。     

机会维修策略下的民机系统维修决策优化模型

以系统单位时间费用率和可用度为目标,构建了民机多部件系统不完全预防性维修多目标优化模型.引入机会维修思想,以部件机会维修可靠度阈值作为模型优化变量,将各部件维修方式按照时间相关性和结构相关性进行优化组合.模型采用改进后的自适应变异粒子群算法(Adaptive mutation particle swarm optimization,AM-PSO)求解Pareto最优解集.利用民机某型发动机反推控制和指示系统实际维修数据进行模型验证与分析,结果表明:在有限运行周期内运用本文的多目标优化模型与方法进行预防性维修活动,能有效降低系统维修成本的同时提高其可用度.     

列车转向架关键部件预防性维修决策优化模型

针对列车转向架关键部件的预防性维修存在欠维修或过维修问题,提出一种列车转向架关键部件预防性维修决策优化模型。该模型以关键部件故障周期为优化变量,基于不同的维修模式和最小修形式确定机会维修期望概率。借鉴机会维修思想,利用最大最小蚁群迭代算法,确定关键部件的最佳维修周期与机会维修阈值,以最小平均费用率为优化目标,最终达到降低预防性维修固定维修成本的目的。以某地铁公司列车转向架关键部件维修为例,通过对比分析证明,该模型能够有效降低转向架关键部件的运营维修费用,减少列车在库停车检修次数,提高列车运营效率,从而为轨道交通装备的维修决策提供理论和实践参考。     

基于平均剩余寿命和Birnbaum重要度的复杂系统静态分组维护策略

多部件系统存在复杂的相关性,制定最优的维修策略使得系统的维修成本最低是科学管理设备的关键之一。提出了一种多部件复杂系统静态分组维修策略。在考虑部件间的经济相关性和结构相关性的基础上,引入平均剩余寿命和结构重要度求得预防性维修阈值。对于事后维修,引入了考虑部件Birnbaum结构重要度的自适应事后维修决策规则。为了找到最优维修策略,采用遗传算法对部件维修费用进行优化。案例分析表明,该方法能够有效降低维修成本。     

多失效系统退化变迁建模与状态维修决策优化

针对具有多个失效模式系统的状态维修决策问题,对多个失效模式竞争发生情况进行分析,研究了多失效模式系统的退化变迁模型,根据状态失效过程估计系统不同失效模式的失效时刻,排序得到系统失效时刻序列。基于此退化描述模型分析多失效模式系统维修策略,构建了系统的期望平均维修费用和平均可用度模型。对构建的多决策变量模型,提出粒子群与单纯型逐步优化方法混合的优化求解算法。以仿真得到的传感器三类失效模式状态数据为例进行算例分析,验证了所提模型及算法的有效性。     

基于半马尔科夫决策过程的风力机状态维修优化

恶劣的工作环境、昂贵的维修成本和停机损失对风力机及其部件的维修提出挑战。以齿轮箱、叶片等风力机核心机械部件为对象,将部件退化过程离散成有限的退化状态;以长期折扣成本最低为目标,考虑风速、备件物流、停机损失等因素的影响,建立基于半马尔科夫决策过程的状态维修优化模型。分析各退化状态下的维修策略、检测间隔时间以及不同退化状态间的转移概率,并采用策略迭代算法求解模型。以某风力机齿轮箱为例,通过对等周期、非等周期检测条件下检测间隔时间和维修成本的分析,得到优化的维修决策。研究结果表明,该模型能有效描述风力机核心部件的退化过程,实现风力机维修优化。     

Optimizing opportunistic preventive maintenance strategy for multi-unit system of CNC lathe

Due to the many components in the multi-unit system (i.e., CNC machine tools), the existing preventive maintenance (PM) strategies conducting independent PM for every component are time-consuming and waste unnecessary maintenance and downtime costs. Hence, this paper introduces the idea of opportunistic maintenance into the PM and proposes an opportunistic imperfect PM approach, which is a more realistic PM policy for a multi-unit system. In this approach, the effect of PM is assumed to be imperfect to correct the failure rate of each unit. According to the corrected failure rate function, the imperfect PM strategy of a single unit is optimized by minimizing the proposed unit maintenance cost rate under the limitation of minimum reliability. Then, these independent imperfect PM strategies for units are rescheduled by the proposed opportunistic PM strategy under the condition of judging the reliability OM threshold of units. We further optimize the opportunistic PM strategy by minimizing the total maintenance cost. Finally, the advantage of our approach is verified by the case study of 18 CNC lathes.

     

Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system

In this paper, we present a new model to find the jointly optimal economic manufacturing quantity (EMQ) and preventive maintenance (PM) policy for a complex production facility. Unlike the previous joint models which dealt with EMQ and maintenance policy considering a single unit production facility and traditional maintenance approaches, we consider a production facility which consists of two modules with economic dependence. The more expensive module (unit 1) is subject to condition monitoring (CM), and only the age information of the second module (unit 2) is available, which follows a general distribution. The deterioration process of unit 1 is modeled as a continuous time hidden-Markov process. CM data is available at the end of each production run, and it provides only partial information about the hidden state of unit 1. The failure state of unit 1 is observable at any time. The objective is to develop a jointly optimal lot sizing and maintenance policy for a two-unit production facility using multivariate Bayesian control approach by minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. Also, a formula for the mean residual life (MRL) of the production facility is derived, which is an important statistic for practical applications. A practical example of the wind turbine CM and maintenance is provided and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.     

Joint control of production, overhaul, and preventive maintenance for a production system subject to quality and reliability deteriorations

This research investigates the case of an unreliable manufacturing system subject to quality and reliability deterioration. In particular, we conjecture that the deterioration of the system leads to a continuous increase in the intensity of failures and a decrease on the quality of the parts produced. As such, deterioration implies a twofold effect on the manufacturing system. When the machine fails, minimal repair is conducted, leaving the machine at the same level of deterioration before failure. Hence, the quality of the parts produced and the failure intensity remain unchanged with this repair. Meanwhile, an overhaul refers to a perfect repair that completely restores the quality of the parts and the failure intensity of the machine. This option completely counters all the effects of the deterioration. Preventive maintenance may also be conducted, but it reduces the level of deterioration only partially, improving the quality of the units produced and the failure intensity just in part. This set of characteristics yields to the formulation of a new control model that simultaneously determines the optimal production plan, the overhaul, and preventive maintenance strategies. Such a joint control policy minimizes the total cost including the inventory holding, backlog, overhaul, preventive maintenance, and defective costs over an infinite planning horizon. Since the dynamics of the system change as a function of the level of deterioration, it is necessary to use its history for a proper formulation; therefore, a semi-Markov decision process is used. Numerical methods are applied to determine the control policy, and numerical examples are conducted as illustrations. An extensive sensitivity analysis is presented in order to confirm the structure of the control policy obtained and examine the effect of several parameters.     

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。     

Optimisation of opportunistic maintenance of a multi-component system considering the effect of failures on quality and production schedule: A case study

For a manufacturing equipment, any unplanned breakdown during the production period results into a high production loss. To keep the manufacturing facilities in good condition, preventive maintenance is planned. However, because of limited time and availability of resources, not all the system components can be or need to be repaired/replaced during a planned opportunity. Hence, the unplanned breakdowns can also be considered as an opportunity to do the maintenance activities for other components to take the advantage of economic dependency in multi-component system. However, when the system is under maintenance, it is very conservative to take the decision of maintenance actions on the components because of limited available time and resources. For such situation, this paper consider an opportunistic maintenance model for a multi-component system to take maintenance decision with a constraint on available time and the system availability requirements. The maintenance decisions for each component involves one of the three actions namely, repair, replace or do nothing to achieve the target availability with minimum maintenance cost. The model also considers the effect of component failures on the quality of product being manufactured as well as the production schedule on the machine. The cost of rejections is considered in the total failure cost along with the maintenance and downtime costs. The production schedule delay factor is considered as a constraint for the maintenance decision to account for the effect on production schedule delay. The optimal solution for the model is obtained using three solution methodologies namely simulated annealing, genetic algorithm and sequence heuristics. Using a real-life example of high pressure die casting machine, the opportunistic maintenance approach is demonstrated and results are discussed.     

工业设备预防性维修策略及其效果评估

采用随机变量评估工业设备预防性维修的效果,以单位时间平均维修费用最低为目标,并假定评估预防性维修效果的参数服从均匀分布,构建维修策略的优化模型并进行数值仿真.仿真结果表明,采用随机参数的预防性维修模型能有效降低故障率增长趋势,减少单位时间平均维修费用,具有更好的优化结果,可以为维修计划的制定和现场的作业调度提供决策支持.     

带缓冲区的三设备串行生产系统动态机会维护策略研究

以采用周定周期预防维护技术并带有中间缓存区的三设备串行生产系统为研究对象,基于机会结余和组成本理论,运用动态规划方法,在组合优化的基础上,建立一种基于设备可靠性的三设备串行系统动态机会维护策略及决策优化模型.该模型克服了在当前生产系统中设备只有短期信息可用,而导致预防性维护计划制定的灵活性不足这一问题.数值试验结果表明,该模型能在控制维护成本的基础上提供有效的维护调度方案,为企业的设备维护管理决策问题提供了一种新的解决思路.     

Optimal periodic replacement policy for a two-unit system with failure rate interaction

In this paper, an economic periodic replacement model for a two-unit system with failure rate interaction between units is presented. In this model, whenever unit 1 fails, it causes a certain amount of damage to unit 2 by increasing the failure rate of unit 2 by a certain degree, while each unit 2 failure induces unit 1 into instantaneous failure. Without failure interaction between units, the failure rates of two units also increase with through an ageing process. The two-unit system is all replaced at age T, or on failure, whichever occurs first. The aim of this paper is to derive the long-run expected cost per unit time by introducing relative costs as a criterion of optimality. The optimal period T^* that minimizes that cost is also discussed. A numerical example is given to illustrate the method.