Opportunistic preventive maintenance optimization for multi-unit series systems with combing multi-preventive maintenance techniques

This paper proposes a dynamic opportunistic preventive maintenance (PM) optimization policy for multi-unit series systems by integrating multi PM techniques. Two PM techniques, periodic PM and sequential PM, are considered. Whenever one of the units reaches its reliability threshold, a PM action has to be performed on that unit. At that time the whole system has to be stopped and PM opportunities arise for the other units of the system. An optimal PM practice is determined by maximizing the short-term cumulative opportunistic maintenance (OM) cost savings for the whole system. Numerical examples are given to show how this approach works. Finally, a comparison between the proposed PM policy and the other policies is given.     

Bayesian reliability assessment and degradation modeling with calibrations and random failure threshold

A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.     

Optimal Burn-in and Preventive Maintenance Modeling with Competing Failure Processes

In this article, we assume that the product in the burn-in procedure only experiences continuous smooth degradation process, while in the field operation period the product experiences both continuous smooth degradation process and shock process. The shock process can cause the product not only traumatic failure,but also additional abrupt degradation damage. After the system reliability model and maintenance model have been proposed, the optimal burn-in setting and age replacement duration are obtained under the considering of minimizing the long run average cost rate. A numerical example with the real data is analyzed to illustrate the application of the model.     

Reliability analysis of multi-state repairable system with dynamic transition probabilities

Inadequate maintenance decisions lead to incremental overall costs. In order to minimize costs in maintenance of the multi-state repairable system, we model a preventive maintenance (PM) scheme of the multistate repairable system using non-Markov process. The periodically decreasing reliability model of the non-Markov dynamic system with dynamic transition probabilities is established to satisfy the probability change. The diesel engine system is taken as an example to illustrate the model. The reliability of the diesel engine is analyzed and its PM scheme is worked out. RENO software is used to simulate the diesel engine system. The maintenance cost of components and the optimal PM interval data of the system are obtained by using the minimal average cost as the objective function. The adaptability of PM is judged, and the optimal PM scheme is presented.     

Modeling research on manufacturing execution system based on large-scale system cybernetics

A cybernetics model of manufacturing execution system(MES CM) was proposed and studied from the viewpoint of cybernetics.Combining with the features of manufacturing system, the MES CM was modeled by"generalized modeling"method that is discussed in large-scale system theory.The mathematical model of MES CM was constructed by the generalized operator model, and the main characteristics of MES CM were analyzed.     

复杂荷载下沥青混合料剪切疲劳性能

采用室内实验模拟不同荷载谱作用下沥青混合料的抗剪切疲劳特性．使用强度退化模型预估随机荷载谱下的沥青混合料剪切疲劳寿命,其结果与实验结果相近．验证了这一模型可为路面使用性能的预测及优化养护决策提供参考．     

Reliability Analysis for the Competing Failure with Probabilistic Failure Threshold Value and Its Application to the k-out-of-n Systems

A method for reliability analysis of the competing failure with the probabilistic failure threshold value not the fixed threshold value is presented, which involves the random shocks and the degradation is independent and dependent respectively. Specifically, for the dependent condition, the effect due to the random shocks on the degradation is considered with a damage factor. In addition, the dependent competing failure model is applied to the reliability analysis of the k-out-of-n systems. Finally, two studied cases are presented to illustrate the proposed method, and the results show the proposed method is reasonable.     

Opportunistic Replacement Optimization for Multi-Component System Based on Programming Theory

It is widely accepted that too excessive or too insufficient maintenance actions on a system are consumptive or potentially risky. This paper focuses on the optimization of opportunistic replacement for a multicomponent system in which no failure or suspension histories can be used for prediction of all the critical components in the system. Firstly, the remaining useful life (RUL) is predicted using the real-time sensor data, which is based on an “individual-based lifetime inference” method. Then a failure risk estimation method is introduced, which is based on the degradation extent and service time of components. Subsequently, the possible replacement combinations of components are compared, which is based on a proposed current-term cost rate. Finally, the best replacement scheduling is selected. The proposed framework is validated by the simulation dataset and PHM-2012 competition bearing dataset. Group replacement and individual replacement are conducted for comparison, and sensitivity analysis is discussed.     

Modeling on spare parts inventory control under condition based maintenance strategy

In order to optimize the spare parts inventory, we present a decision-making model under condition based maintenance policy for a single equipment system subjected to continuous and random deterioration. Firstly, a probability model of the spare parts support is established, according to the requirement of a predetermined probability of stockout. It can determine the optimal spare parts stock level. Secondly, the spare parts ordering decision is made according to the equipment deterioration level, and it can optimize the spare parts ordering. The objectives of this model are to minimize the spare parts inventory, and the expected total operating cost. Thirdly, a numerical example is given to illustrate this model. The results prove that the optimal preventive maintenance threshold obtained from the proposed model can satisfy the spare parts support requirements.     

铁路钢轨网格化维修规划优化编制模型

优化编制铁路钢轨维修规划,对管理者合理利用维修资源、确保铁路行车安全具有重要意义.本文基于铁路基础设施网格化管理思想,按照一定规则将线性、连续的铁路钢轨设备,分割为等长的小区段,这些小区段称为钢轨网格.以钢轨网格为基本对象,采用多阶段指数Markov模型,个性化描述钢轨网格状态变化规律;在此基础上,采用Markov决策过程方法,以规划周期内的期望总成本最小为目标函数,建立基于状态的维修策略模型,确定一个较长规划周期内的最优维修策略.作者采用了陇海线实际数据对提出模型的有效性进行了验证,计算结果表明,本文提出的模型优于基于阈值的中国铁路既有的管理方法.     

Proactive Approach for Production and Condition-Based Maintenance Integration Problem in a Deteriorating System

A proactive approach is constructed to cope with the integrated problem of batch production and maintenance in a deteriorating system. The condition of the system is modeled by a proportional hazards model(PHM) which considers both system deterioration state and usage. The deterioration state of system is uncertain and is only observed between batches. An integration model for optimizing production plan and conditionbased maintenance(CBM) policy is proposed, in which the maintenance threshold and production quantity are proactively decided simultaneously. To obtain a robust solution with minimal cost over the planning horizon, a simulation-based iterative algorithm is developed to solve the complicated non-linear model. Numerical results show that the performance of the developed approach is satisfactory under uncertainty.     

Optimal allocation of fleet’s spare parts for system reliability

Allocation of fleet’s spare parts is rarely studied due to its complexity. However, this task is extremely important because the warship’s service level highly relies on the maintenance logistics’ level. In this study, the readiness ratio is proposed as a critical index in measuring the system’s reliability. A well-established mathematical model adopting the optimization method of spare part allocation is also introduced. The objective is to minimize the number of each spare part while satisfying the fleet’s system reliability. The fault tree analysis (FTA) is applied to analyze the system’s failure logic and stratify the units on ship. As a result, the strategy of spare part sharing can be introduced in detail. The solution algorithm is developed, and the simulation experiments to obtain the key parameters are conducted. The proposed model and algorithm are applied to an actual fleet of two warships, and results show that the method above is feasible and can be directly applied into practice.     

动车组单部件两级非完美维护策略及优化研究

对动车组部件的维修保养是保证车辆在运营期间具有一定可靠度的重要措施,其维护周期的确定直接影响铁路运输安全.基于我国动车组的多级维护制度,提出部件的多等级非完美维护策略.选取动车组四级修时更换的部件为研究对象,建立了一种基于里程故障威布尔分布的两级非完美维护模型.以我国现行的动车组维护计划为框架,提出了动车组部件的两种维护方案,将维修时间和维修成本作为优化目标,应用多目标遗传算法对研究区间内的维修周期和所需的维修等级进行决策,并与单一非完美维护模型的优化结果进行比较.研究结果表明,两级非完美维护策略能以更低的维护成本得到满足可靠度要求的维护计划,且在不严格匹配整车维修等级的部件维护方案下得到的维护计划能够在较少的维护成本和维护次数下使部件处于较高的可靠度,更具经济性和安全性.      

资源约束下城市轨道交通基础设施维修任务安排优化模型

针对城市轨道交通基础设施系统组成复杂、维修任务繁多、维修资源有限的特点,在分析不同类型维修任务需求的基础上,研究资源约束下基础设施维修任务的长期安排方法。考虑作业工队、维修设备等限制条件,建立基于混合整数规划的优化模型以安排任务的开始时间和持续时间,实现任务执行费用和惩罚费用的最小化。通过引入作业时间占用约束以减少任务作业冲突并提高维修任务安排的可实施性。针对约束复杂的大规模问题,设计综合求解器和启发式方法的并行混合算法进行求解。案例研究表明：考虑维修任务作业时间对提高维修任务安排的可实施性具有重要作用,所构建模型可有效协同安排不同类型的维修任务并节省维修费用,为城市轨道交通基础设施的长期维修任务安排提供决策支持。     

基于图正则化和Schatten-p范数最小化的交通数据恢复

为充分利用交通数据低秩特性与局部近邻关系，准确恢复交通数据采集系统中的缺失数据，首先，应用基于核范数的低秩矩阵补全模型对交通数据矩阵进行预插补，以获得缺失值的初始估计，基于此，构建表征数据局部近邻结构的图模型；然后，提出融合图正则化和Schatten-p范数最小化的交通数据缺失值恢复模型；进一步，提出基于交替方向乘子框架的优化算法，求解缺失值恢复的最优化问题，得到最终的数据恢复结果；最后，用实际的高速公路交通流量和速度数据比较多种方法的恢复误差，同时给出所提方法的参数敏感性分析. 实验结果表明:在完全随机缺失、随机缺失和混合缺失模式下，缺失率为10% ~ 50%时，相比于局部最小二乘、概率主成分分析和低秩矩阵补全等方法，基于图正则化和Schatten-p范数最小化的算法恢复误差降低了3.02% ~ 28.49%.      

铁路重载货车RCM管理体系理论框架研究

针对铁路重载货车管理体系存在的单一的定期维修方式问题,提出铁路重载货车的以可靠性为中心的维修（RCM,Reliability Centered Maitenance）管理体系．该体系通过故障模式和影响分析方法对重载货车产品出现故障后对运行的影响程度进行可靠性分析,确定重要功能产品,对重要功能产品的每一种故障模式做出维修决策及相应的维修措施,并根据实际运行中得到的反馈信息不断地对重载货车产品进行维修决策优化,进而对维修策略进行修订和完善,形成一个良性循环的管理体系,最终实现重载货车产品维修工作的科学化、系统化和规范化管理,全面提高重载货车管理水平,切实保障重载货车产品的高运行可靠性,同时也实现了维修思想由“以预防为主”到“以可靠性为中心”的转变．     

基于两阶段算法的运行图与天窗协同优化

列车运行图铺画与天窗设置存在相互影响，相互制约的耦合关系，为了达到优化列车运行图结构，合理配置铁路运力资源的目的，在分析天窗与列车运行动态影响关系的基础上，以天窗设置对列车运行线铺画影响最小为目标，建立了列车运行图与天窗协同优化的混合整数规划模型. 考虑问题复杂性,设计了包含初步优化和综合优化的两阶段求解算法. 初步优化阶段采用基于专家经验的启发式算法得到列车运行图的大体框架，综合优化阶段利用禁忌搜索算法获取全局最优解. 最后以宝成线（阳平关—成都）为例进行有效性验算. 结果表明，相较于人机交互编制所得运行图，优化得出的运行图中所有客货列车在途经车站的总停留时间降低了6.19%，共减少1 355 min，其中旅客列车和货物列车在站停留时间分别降低了3.08%和7.40%，减少总时间分别为189 min和1 166 min.      

隔振抗冲击系统组合优化设计方法

研究了隔振和抗冲击的组合优化设计问题.指出连续性设计思想的弊端,提出组合优化设计方法并建立了系统模型;分析了冲击隔离系统的极限性能及2种实现方法,一种是采用较大阻尼的单个线性元件;一种为无阻尼的多个线性刚度元件.仿真和试验结果表明:在缓冲器的刚度远大于隔振器的刚度时,具有线性刚度和阻尼的缓冲元件最佳阻尼比为0.4的结论仍然成立;通过结构优化设计.无阻尼线性元件同样能够实现性能最优化.     

考虑行程时间波动的电动公交可变行车计划

为解决因运行时间不确定性导致的公交到发时间不准点问题,本文基于公交线路双方向发车趟次和运营时间的不对称特征,提出一种可变行车计划优化问题。以最小化车辆使用数和乘客等待时间为目标,考虑车次链的行程接续和电动公交车辆电量等约束,构建公交时刻表和车辆排班一体化优化模型。根据可变行车计划优化问题特性设计改进的粒子群算法(Modified Particle Swarm Optimization for Timetabling and Scheduling, MPSO-TS)进行求解,定制粒子编码和子代更新方式。采用“基于优势车次链”的子代更新机制,以“车次链”为纽带最大程度地保留父代被继承信息中时刻表与车辆调度方案之间的关联性。使用连云港市某公交线路验证模型和算法,案例结果表明：可变行车计划能够有效保证车辆到发准点性,通过更紧密的排班计划将使用车数由35辆减少至31辆,车辆使用效率提升了28.1%;所提出的MPSO-TS算法求解效率较高,具有较好的稳定性,可有效避免计算结果陷入“局部最优”。     

广义Gauss模型及其模拟退火算法

研究了求解优化问题全局解的随机神经网络方法，将Gauss模型拓展为广义Gauss模型，使之能求解一般优化问题的全局解。进而引入全局性较好的模拟退火算法的思想，提出了广义Gauss模型的模拟退火算法。通过算例比较了几种计算智能算法的全局性。广义Gauss模型的模拟退火运行全局性最好，但它付出了时间的代价；广义Gauss模型兼顾了计算效率和全局性；广义Hopfield网络的全局性最不理想。