Joint optimization of inspection, maintenance, and spare ordering policy considering defective products loss

This paper proposes a joint inspection-based maintenance and spare ordering optimization policy that considers the problem of integrated inspection,preventive maintenance,spare ordering,and quality control for a four-state single-unit manufacturing system.When an inspection detects a minor defect,a second phase inspection is initiated and a regular order is placed.Product quality begins to deteriorate when the system undergoes a severe defect.To counter this,an advanced replacement of the minor defective system is carried out at the Jth second phase inspection.If a severe defect is recognized prior to the Jth inspection,or if system failure occurs,preventive or corrective replacement is executed.The timeliness of replacement depends on the availability of spare.We adopt two modes of ordering:a regular order and an emergency order.Meanwhile,a threshold level is introduced to determine whether an emergency order is preferred even when the regular order is already ordered but has not yet arrived.The optimal joint inspection-based maintenance and spare ordering policy is formulated by minimizing the expected cost per unit time.A simulation algorithm is proposed to obtain the optimal two-phase inspection interval,threshold level and advanced replacement interval.Results from several numerical examples demonstrate that,in terms of the expected cost per unit time,our proposed model is superior to some existing models.

Availability-based maintenance optimization under unequal inspection period and imperfect repair

Taking the multi-component system as research object, a maintenance optimization model based on the unequal inspection period and imperfect repair is established by considering the requirement of expected availability for improving the system＇s availability. An age reduction factor is used to describe the effect of imperfect repair, and the modelling approach for the unequal inspection period is proposed. Unavailable situations are classified into three kinds of independent cases, and the availability is calculated accordingly. Based on the analysis of the relationship between the unavailable cases and the unequal inspection period, an optimization model under imperfect repair is established to optimize the system＇s expected availability. A case study of a wind turbine is provided, and three key components, i.e. gearbox, generator and spindle, are considered. The optimization results of the unequal inspection period model and the equal inspection period model are compared. The results show that the unequal inspection period model based on availability can update the maintenance plan so as to optimize maintenance activities and improve the system＇s availability.

DELAY-TIME MODEL BASED ON IMPERFECT INSPECTION OF AIRCRAFT STRUCTURE WITHIN FINITE TIME SPAN

According to the failure characteristics of aircraft structure, a delay-time model is an effective method to optimize maintenance for aircraft structure. To imitate the practical situation as much as possible, imperfect inspections, thresholds and repeated intervals are concerned in delay-time models. Since the suggestion by the existing delay-time models that the inspections are implemented in an infinite time span lacks practical value, a de- lay-time model with imperfect inspection within a finite time span is proposed. In the model, the nonhomogenous Poisson process is adopted to obtain the renewal probabilities between two different successive inspections on de- fects or failures. An algorithm is applied based on the Nelder-Mead downhill simplex method to solve the model. Finally, a numerical example proves the validity and effectiveness of the model.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-to-failure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs;second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

Realization of an Optimal Dynamic Geodetic Reference Frame in China:Methodology and Applications

China Geodetic Coordinate System 2000(CGCS2000)has been used for several years as a formal published reference frame.The coordinates of all global navigation satellite system(GNSS)stations in China need to be corrected to align with the CGCS2000 frame.Different strategies can be adopted for the realization of an optimal reference frame.However,different strategies lead to different results,with differences as great as several decimeters when GNSS station coordinates are transformed into CGCS2000-defined coordinates.The two common methods for the coordinate correction of a GNSS station are quasi-stable adjustment under CGCS2000 and plate movement correction,and the differences between their results can be greater than 10 cm.In this study,a statistic method called"supervised clustering"is applied to the selection of GNSS reference stations;a new scheme named"partition spacing"for the grouping of all processed GNSS stations is proposed;and the plate movement correction method is used to correct the coordinates of all GNSS stations from the GNSS epoch to the CGCS2000 epoch.The results from the new partitioning method were found to be significantly better than those from the conventional station-blocking approach.When coordinates from the stations without grouping were used as the standard,the accuracy of all the three-dimensional coordinate components from the new partitioning method was better than 2 mm.The root mean squares(RMSs)of the velocities in the x,y,and z directions resulting from the supervised clustering method were 0.19,0.45,and 0.32 mm∙a1,respectively,which were much smaller than the values of 0.92,0.72,and 0.97 mm∙a1 that resulted from the conventional approach.In addition,singular spectrum analysis(SSA)was used to model and predict the position nonlinear movements.The modeling accuracies of SSA were better than 3,2,and 5 mm in the east(E),north(N),and up(U)directions,respectively;and its prediction accuracies were better than 5 mm and 1 cm for the horizontal and vertical domains,respectively.

Risk Based Optimal Inspection and Repair Planning for Ship Structures Subjected to Corrosion Deterioration

A framework of risk based inspection and repair planning was presented to optimize for the ship structures subjected to corrosion deterioration. The planning problem was formulated as an optimization problem where the expected lifetime costs were minimized with a constraint on the minimum acceptable reliability index. The safety margins were established for the inspection events, the repair events and the failure events for ship structures. Moreover, the formulae were derived to calculate failure probabilities and repair probabilities. Based on them, a component subjected to corrosion is investigated for illustration of the process of selecting the optimal inspection and repair strategy. Furthermore, some sensitivity studies were provided. The results show that the optimal inspection instants should take place before the reliability index reaches the minimum acceptable reliability index. The optimal target failure probability is 10 -3 . In addition, a balance can be achieved between the risk cost and total expected inspection and repair costs by means of the risk-based optimal inspection and repair method, which is very effective in selecting the optimal inspection and repair strategy.

Virtual Reality Technology Applied on Maintenance of Painted Walls of Buildings

In a building, the paint coating applied to interior walls conveys their aesthetic character and also performs an important function of protection. It is a construction component which is exposed to agents of deterioration related to its use, needing the regular evaluation of its state of repair. The completed model supports the performance of such periodic inspections and the monitoring of interior wall maintenance, using Virtual Reality (VR) technology. Used during an inspection visit, the application allows users to consult a database of irregularities, normally associated with paint coating, classified by the most probable causes and by recommended repair methodologies. In addition, with this model, a chromatic scale related to the degree of deterioration of the coating, defined as a function of the time between the dates of the application of the paint and the scheduled repainting, can be attributed to each element of coating monitored. This use of VR technology allows inspections and the evaluation of the degree of wear and tear of materials to be carried out in a highly direct and intuitive manner. The computer application has a positive contribution to make in the field of construction, using as it does Information Technology (IT) tools which give access to innovative technology with it capacity for interaction and visualization.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines (WTs) to avoid breakdowns andreduce maintenance costs. Many maintenance models have been developed for WTs’ maintenance planning, suchas corrective, preventive, and predictive maintenance. Due to communities’ dependence on WTs for electricityneeds, preventive maintenance is the most widely used method for maintenance scheduling. The downside tousing this approach is that preventive maintenance (PM) is often done in fixed intervals, which is inefficient. In thispaper, a more detailed maintenance plan for a 2 MW WT has been developed. The paper’s focus is to minimize aWT’s maintenance cost based on a WT’s reliability model. This study uses a two-layer optimization framework:Fibonacci and genetic algorithm. The first layer in the optimization method (Fibonacci) finds the optimal numberof PM required for the system. In the second layer, the optimal times for preventative maintenance and optimalcomponents to maintain have been determined to minimize maintenance costs. The Monte Carlo simulationestimates WT component failure times using their lifetime distributions from the reliability model. The estimatedfailure times are then used to determine the overall corrective and PM costs during the system’s lifetime. Finally,an optimal PM schedule is proposed for a 2 MW WT using the presented method. The method used in this papercan be expanded to a wind farm or similar engineering systems.

An Optimal Method to Schedule Dynamic Maintenance Task with Subject Taken into Account

The task of maintenance organization is very heavy at wartime.The usability of armaments may be greatly improved by efficient task scheduling.In order to recover the battle effectiveness of units in battlefield as fast as possible,dynamic maintenance scheduling models with subject taken into account were built on the basis of analysis the feature of maintenance task.Maintenance task scheduling problem is very complicated.So it is decomposed into two sub-problems:static maintenance task scheduling and dynamic maintenance task scheduling problem with subject taken into account.Corresponding mathematic models were built to these sub-problems and their solutions were proposed.Dynamic maintenance task scheduling with subject taken into account is on the basis of static maintenance task scheduling.With the task changing in battlefield,dynamic task scheduling can be realized by repeatedly call of static maintenance task scheduling with subject taken into account.The experimented results show that dynamic maintenance task scheduling method with maintenance subject taken into account is valid.

基于SIR模型重构医院保障设备监控预警系统

医院智能楼宇的建设离不开精细化的管理。后勤系统众多,对各系统运行状态进行实时监控,保障医疗秩序的安全稳定开展,需要楼宇自控系统合理分配监控点位。点位分布的过多,会造成成本增加;分布过少,起不到全面监控的效果。借助管理工具查找预警信息规律,找出系统运行风险的阈值和极值,实现点位重构,并对不同风险程度的点位采取不同的管理措施,从而达到降低成本、降低风险、优化运维的最终目标。

基于FMEA分析和可靠性建模计算的核电厂楔形闸阀预防性解体维修策略优化研究

以核电厂某核岛系统楔形闸阀为例,结合其使用环境、结构特点和运维检修数据分析,基于FMEA分析、风险管控评估和可靠性建模计算,对其预防性解体维修周期进行了优化评估。研究结果表明:该楔形闸阀具备解体周期延长条件,阀门解体周期延长对缩减核电厂不必要的维修资源投入、降低维修辐射剂量,以及缩短大修关键路径有直接贡献。

地铁隧道病害检测深度学习模型优化及应用

本文针对地铁隧道中渗漏水、裂纹裂缝、结构抹灰开裂及剥落掉块4种常见病害,研究了基于激光雷达扫描点云数据和深度学习的地铁隧道病害检测方法。首先,引入ACmix注意力模块,使网络兼顾全局特征和局部特征,提升对裂纹、裂缝等小目标的检测效果;然后,优化回归损失函数,提高收敛平稳度和回归精度,降低检测误差;最后,实现正射投影图像预处理、批量检测、结果融合及检测结果报表的一体化生成,提高大尺度正射投影图的病害检测率。试验结果表明,在选取IoU阈值为0.5的条件下,改进后的YOLOv8算法在隧道病害测试检测中正确率由90.65%提升至91.18%,基本实现了基于激光雷达扫描的地铁隧道4类常见病害的智能检测,并在实际隧道运维工程中得到成功应用。

计及可达时间窗的远岸风场运维路线优化策略

通过构建风浪预测模型求解风电机组可运维时间窗,作为调度优化的时间约束。为最小化运维成本,考虑运维过程中服务母船(SOV)与船员转运子船(CTV)之间的运维调度问题。将不同技术人员划分为几个独立的运维班组,以运维班组技术水平、待运维风电机组故障类型、动态运维时间窗口、运维班组可连续作业时长和运维时间窗为约束条件,构建SOV-CTV运维路线优化模型。以30台待运维风电机组为仿真案例对该模型的有效性进行测试,结果表明,提出的模型可有效缩短运维总路径长度和降低运维成本,提高了运维班组的运维效率。

微型电机声学质量检测小样本机器学习方法研究

为了解决微型电机声学质量检测人工手摸及听诊方法存在的主观误判率高、效率低下等问题,同时兼顾检测结果准确率和检测模型构建的快速性,提出了一种小样本机器学习检测方法,其根据微型电机传动链物理模型进行多维声学故障特征提取,在此基础上,采用粒子群优化算法对支持向量机这种小样本学习方法的核心参数进行优化,从而提高模型判别的准确率。试验结果表明,该方法能够有效判别微型电机异常振动和声音,准确率达到95%以上。

基于改进灰狼优化的桥梁检测爬壁机器人全覆盖路径规划

基于爬壁机器人对混凝土桥梁健康进行自动巡检是推动桥梁管养智能化的有效途径,而合理的路径规划对机器人全面获取检测数据尤为重要。针对爬壁机器人电源重量限制与巡检时能源补充困难这一工程实际问题,充分考虑主梁、高墩等桥梁构件巡检场景,将能量消耗指标作为性能评价优化目标函数并建立相应约束条件,进而提出全覆盖路径规划评价模型。针对传统灰狼优化(GWO)算法易陷入局部最优的不足,提出一种改进的灰狼优化(IGWO)算法。IGWO算法通过K-means聚类改善了灰狼初始种群在搜索空间难以保持相对均匀分布的特性;以非线性收敛因子提高算法局部开发能力和全局搜索性能;结合粒子群算法个体优越性的思想对位置更新公式进行改进,提升算法的模型求解能力。仿真对比实验结果表明,IGWO算法相较于GWO、差分进化(DE)与遗传算法(GA)等全局优化算法,稳定性更好,能耗降低了10.2%~16.7%,迭代次数与求解时间分别减少了19.3%~36.9%和12.8%~32.3%,路径重复率降低了0.23~1.91个百分点,同时路径长度缩短1.6%~11.0%。

基于智能优化算法的配电自动化系统运维管理分析

阐述基于智能优化算法的配电自动化系统运维管理系统设计,探讨该系统运维管理策略,包括基于改进遗传算法、基于改进人工蜂群算法、基于改进布谷鸟算法的配电自动化系统运维策略。

基于专业相关系数的维修人员配置优化模型

装备维修专业人员是战时装备维修保障活动的主体,但目前关于战时维修人员优化配置的研究与实际情况还存在差距。以高功率微波武器装备为例,首先以各维修专业需求工时为标准,预测维修机构战时面临的维修任务量;其次,在考虑有效维修时间的基础上,分析估计维修机构的维修能力;再次,在各专业能完成战时相应维修任务的条件下,预计各维修专业人员需求;最后,利用专业相关系数对各维修专业人员进行优化配置,并通过已知维修任务对各维修专业工时需求实例,验证了维修专业人员优化配置模型的有效性。

石油化工生产设备检维修的优化方法分析

为了减少较为繁琐的石油化工生产流程,本文针对石油化工生产过程中设备检修的重要性进行了分析,并提出了相应的风险管理和防控措施。石油化工企业应综合考量管理人员技能、检修管理体系的完善性和设备的运行状态,以保障设备检修的全面性和合理性,实现可持续发展目标。通过采取上述措施,石油化工企业可以降低设备故障风险,提高生产效率和产品质量,同时确保员工的安全和环境保护。