Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.     

Integrated maintenance and operations decision making with imperfect degradation state observations

In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.     

可用度约束条件下的设备不完美预防维修策略

针对2M1B生产系统,基于设备实际维修情况,提出了故障设备不完美预防维修策略。首先,考虑设备随时间不断劣化的情况,基于准更新过程,建立了生产周期内设备随机故障次数的表达式,计算了设备维修总费用。其次,通过分析缓冲区内库存的变化轨迹,以生产周期内设备随机故障次数为基础,计算了缓冲区库存费用,综合设备维修费用和缓冲区库存费用,构建了周期内生产总成本模型。以满足系统最低要求的可用度水平为约束条件,以预防维修周期和缓冲区库存量为决策变量,以生产周期内单位时间总成本为目标函数,通过离散迭代算法获得最优预防维修周期和此周期下的最佳缓冲区库存量。最后,通过数值分析验证了模型的有效性,为制定最佳维修策略提供了有效依据。     

基于仿真的生产设备预防性维修周期研究

为了对生产系统中的设备故障加以有效的控制，从而减少设备故障的发生，文章对生产设备的预防性维修周期问题进行了研究。通过建立生产设备预防性维修的费用模型，利用仿真的方法对不同维修策略的预防性维修周期进行优化选择。该模型综合考虑了修复性维修成本、预防性维修成本和生产损失成本。最后以故障分布形式为威布尔分布的设备为例进行仿真实验，得出设备最优维修周期结果，并阐述了经济原则对预防性维修周期的影响。     

板级自动测试系统在航空电子装备中的应用

航空装备大修厂承修的机载电子装备中,常遇到较早期设计的电路板产品,这类产品相对而言可测性较低,测试方案不明确且产品维修资料较少,若遇到因电路板故障引起装备失效的情况,维修周期较长且故障排除效果不稳定,对维修单位而言在电路板级故障诊断带来很大困难。国内早期板级自动测试系统的研究偏向于专板专用,专业性较强通用性较弱,与实际维修保障应用方面相结合的案例较少,本文针对这一短板,对通用性较强的板级产品自动测试系统及其在航空电子装备中的应用进行了研究,选取故障较高的视频信号处理板为样本,用实例阐述了如何以板级测试系统为平台,确定了板级故障诊断设计方案,设计了测试开发流程,增强了维修行业对板级故障诊断技术的完备性与多样性。     

Estimation of active maintenance opportunity windows in Bernoulli production lines

Effective and timely maintenance actions can sustain and improve both system availability and product quality in automated manufacturing systems. However, arbitrarily stopping machines for maintenance will occupy their production time and may introduce system-level production losses. There may exist hidden opportunities during production, such that specific machines can be actively shut down for preventive maintenance without penalizing the system throughput. In this paper, the time intervals for such opportunities are defined as active maintenance opportunity windows (AMOWs). A Bernoulli model is developed to analytically estimate AMOWs in two-machine-one-buffer production lines. A recursive algorithm based on an aggregation method is used to estimate AMOWs in long lines. For balanced production lines, a heuristic algorithm is proposed to estimate AMOWs in real time. The effectiveness of the methods has been validated through numerical studies.     

ETL模型在配电生产运行风险管控信息平台建设中的应用

电网公司配电生产运行风险管控信息支撑平台需要各种在线监测数据、离线数据等大量的配电网运行状态数据,这些数据往往分布在不同的应用系统中,例如停电检修计划（OMS）,电力生产管理系统（PMS）,在线监测系统,地理信息系统（GIS）,SDADA等系统,这些系统在建设的初期往往没有考虑到系统数据的横向集成,因此各业务的业务模型和数据的标准类型存在着较大的差异,为了实现配电生产运行的状态数据信息的有效准确提取,文章以ETL（Extract Transform Load）模型为研究基础,并在此基础上给出了一种通用化的数据抽取、转换和装载的方法,实现了从不同业务系统中对不同数据来源的数据进行抽取、转换和装载工作,为配电生产运行状态的风险管控工作提供了所需要的生产运行状态数据集,进而能够为保证配电生产正常运行提供有力的保障。     

基于维护计划的卷烟生产线设备运维管理系统

介绍了一个基于维护计划的卷烟生产线设备运维管理系统,分别从系统功能模型、计划业务流程等角度描述了系统的设计思想。并阐述了系统涉及的设备润滑计划制定、派工、运行维护数据分析、系统开发与应用等关键技术。开发的系统具有一定的可集成性、可扩展性和可重构性,与移动通讯技术、数据采集技术相结合,集成在制造执行系统中,可以实现卷烟生产企业制丝、卷包生产线的实时设备运行与维护保养管理。     

需求随机波动下多设备批量生产系统的视情维护策略研究

针对需求随机波动情况下多设备批量生产系统的设备维护问题,提出了一种基于滚动生产计划和设备退化状况的视情维护策略。首先,通过滚动时域规划方法预测不同产品的随机需求并在此基础上以总生产成本最小确定滚动生产计划。其次,在每一滚动生产周期开始前检测系统中各设备的退化水平,利用Gamma过程描述退化增量,以最小维护成本率确定当前退化状态下各设备的最佳维护时间,同时为避免生产过程中断利用提前延后维护策略对预防维护进行动态调整。在系统层,利用生产转换时机对需要维护的组件进行组合维护。再次,引入时间约束和服务水平约束,建立批量生产与视情维护的联合优化模型,以总成本最小为目标,确定实际生产计划和维护计划。最后,通过算例以整个生产计划期内的总成本和故障次数为度量验证了所提出的多设备批量生产系统视情维护策略的有效性。     

国家水资源省级项目运维系统现状分析与发展探讨

为了各省能有效地管理和运维水资源监控系统,对国家水资源监控能力建设项目运维管理工作面临的站点数量庞大、种类繁多、硬件设备和业务系统复杂等基本情况和主要问题进行分析。在此基础上对运维管理系统和运维服务方式的发展趋势和要求进行探讨,并提出加强水资源监控管理信息平台运维系统建设、完善管理与运行维护机制、健全运维绩效评估体系、运用新技术提升运维管理效率等建议,确保其长期有效运行并发挥效益。     

某型雷达虚拟维修训练系统的设计与实现

为提高教学训练效果，弥补装备教学训练中实装不足、装备损耗大、教学训练成本高等实际问题，设计并实现了某型雷达虚拟维修训练系统。该虚拟维修训练系统包括系统主界面和系统集成框架两大部分，具有装备结构展示、装备操作训练、装备维修训练、训练效果评估、数据支持等多项功能，有效解决了上述装备教学训练中存在的实际问题。目前，该系统已应用到相关专业的装备构造与维修、装备综合实践等多个课程当中，取得的很好的教学训练效果，系统的设计开发可为其他装备虚拟维修训练系统的开发提供参考与借鉴。论文首先介绍了系统的基本功能和结构，然后详细论述了系统模型的开发过程，最后给出了虚拟训练工作流程。     

An optimal production lot-sizing problem for an imperfect process with imperfect maintenance and inspection time length

This article develops an integrated model in considering the situations of an imperfect process with imperfect maintenance and inspection time for the joint determination of both economic production quantity (EPQ) and preventive maintenance (PM). This imperfect process has a general deterioration distribution with increasing hazard rate. Even with periodic PM, such a production system cannot be recovered as good as new. This means that the system condition depends on how long it runs. Also, the PM level can be distinct due to the maintenance cost. For convenience, it is assumed the age of system is reduced in proportional to the PM level. Further, during a production cycle, we need an inspection to see if the process is in control. This inspection might demand a considerable amount of time. In this article, we take PM level and inspection time into consideration to optimise EPQ with two types of out-of-control states. To see how the method works, we use a Weibull shock model to show the optimal solutions for the least costs.     

带缓冲区且产品保修的设备预防性维护建模

论文考虑的是一个完美的生产系统,即生产系统所生产的产品均没有质量问题。为提高设备的可靠性,调整市场需求,提出带缓冲区的设备预防性维护优化建模方法。已经出售的产品,顾客在使用过程中可能会出现故障,基于这个情况,在保修期内会予以免费维修。找出生产系统的运行时间(T)和缓冲区库存量(S),以此为决策变量建立期望成本模型。在此基础上,构造求解最优策略的算法。通过数值算例演示了该模型,通过灵敏度分析确定了最优策略和相关参数的关系。     

A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.     

基于RFID的制冷设备分级维护系统

针对制冷设备的维护提出了一种基于射频识别(RFID)技术的分级维护系统,第一级是制冷设备控制器实时故障诊断的在线诊断点,第二级是结合故障树模型全面分析制冷设备故障原因的离线诊断站,第三级是对制冷设备大量历史状态数据分析实现产品优化升级的智能维护中心。实际应用表明,系统能很好地提高维护效率,降低维护成本。     

Planning and optimizing the maintenance of paper production systems in a paper plant

Manufacturing and production plants operate physical assets that deteriorate with usage and time, thus, maintenance actions are required to restore the assets back to their original predetermined operational conditions. But since, organizational resources are limited and scarce. The objective of an effective maintenance program is to minimize total cost of inspection and repair, and equipment downtime. In this paper, we describe a new multi-criteria optimization framework for deriving optimal maintenance schedules for preventive maintenance which considers availability, maintenance cost and life cycle costs as the criteria for optimization. The Simulink toolbox of the Matlab has been interfaced with Genetic algorithm for optimization. After getting solution, Stochastic Petri nets has been used to model the system and find out the effect of optimized maintenance schedules on system performance. The application of the proposed framework has been discussed on a practical case of a paper plant.     

卫星测控站监控系统软件结构的研究与实现

测控站监控系统由于系统规模小、设备复杂,缺少商业化控制软件的支持,因而建造成本高,周期长,缺少灵活性和扩展性,系统维护困难。不适应航天技术发展的需要。通过分析测控站监控与一般工业控制的差别,借鉴组态软件设计思想,设计面向对象的设备对象模型和显示模型,为设备对象定义设备参数、数据处理函数、通信接口参数等属性,为显示元素定义静态和动态的图形属性、显示方法以及与设备对象的关联。依据配置文件,在运行时,生成设备对象。完成设备数据的接收、处理和控制;每个显示配置文件形成一幅显示画面;显示元素通过关联的设备参数更新显示属性。这种方法为在特定领域内构建监控系统提供了经济实用的解决方案。     

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.     

融入工作流技术的煤矿机电设备EAM系统设计

针对现阶段煤矿设备管理所面临的维护与维修成本不断增高、管理灵活性差等问题,融合工作流技术与EAM思想设计了煤矿机电设备EAM系统。该系统采用工作流技术对采购模块等业务性很强的业务过程进行流程优化,并将点检闭环管理、全生命周期管理等EAM基本思想贯穿于煤矿设备管理系统中。实际应用表明,该系统有效提升了煤矿企业设备管理效率,降低了企业运营维护成本。     

物联网技术在装备维修备件管理中的应用

装备维修备件管理是装备维修管理的基础和先决条件,传统的维修备件管理方法已不适应信息化技术的发展。为了促进装备维修管理的信息化建设,提高管理的信息化、自动化及智能化程度,在备件管理中引入物联网技术,通过对现行维修备件管理方法的缺点及维修备件管理对物联网的需求的简要分析,对物联网应用于装备维修备件管理系统中的主要功能进行了初步的设计和探索。