单机调度与非完美视情维修的联合决策与优化

在生产加工过程中,考虑维修计划和生产调度的相互依赖作用,提出了作业调度和非完美预防性视情维修的联合策略。针对状态可检测的可修单设备生产系统,以作业调度序列和视情预防性维修阈值为决策变量,最小总加权期望完成时间为优化目标,建立了联合决策模型。对相应的概率密度函数进行推导,并用数值方法进行求解。通过数值分析,验证联合决策相对于独立决策的优势,并进行参数的灵敏度分析,表明模型的有效性。     

累积冲击下的经济生产批量及视情维修联合优化问题

为解决传统基于设备不会失效假设下经济生产批量问题研究的不足，考虑经济生产批量系统中设备失效维修的问题，提出了生产批量和视情维修联合优化的方法。基于泊松过程和一般分布构建冲击到达过程及累积冲击效应，结合设备退化观测和具体的维护费用结构，给出了可修退化系统最优的生产批量和视情维修阈值的求解模型。通过数值分析验证了最优策略的有效性，并进行了参数的敏感性分析，为设备管理者提供决策依据。     

马尔可夫决策过程在视情维修中的应用

在实际应用视情维修中，维修时机的确定通常是通过经验或简单的趋势检验，其结果比较粗糙，不便于应用。在本文中，我们研究了应用马尔可夫过程描述系统劣化的过程。针对维修与不维修两种决策行为，以单位时间费用最低为目标的决策过程，并给出了具体算法计算维修状态阈值。最后，通过一个例子说明了决策的过程。     

基于剩余寿命预测的维修与备件订购联合策略优化

利用设备健康状态信息预测剩余使用寿命,并进行维修和备件订购决策以达到降低设备检修成本和备件成本的目的。针对单部件系统提出基于剩余寿命预测的维修与备件订购联合策略,其中维修决策遵循控制限原则,即根据系统退化量判断是预防性更换还是故障更换,同时基于历史退化信息预测系统剩余寿命,引入订货阈值判断是否订货。通过分析更换时刻备件状态确定所有可能更新事件,推导各事件发生概率进而计算各事件更新成本和更新长度,采用更新报酬理论构建最小化单位时间内期望费用的联合策略模型,设计离散事件仿真算法求解模型。最后,通过实例验证模型和算法,得到最小的单位时间内期望费用14.656 3,最优预防性更换阈值8,最优订货阈值1 000。     

民航视情维修决策优化模型发展

目前视情维修是民航业采用的主要维修方式，可以使维修的有效性、经济性大幅度提高。维修决策优化模型对民航维修成本的降低和利润的提升具有显著的指导作用；归纳了视情维修的时间延迟模型、冲击模型、比例危险模型、马尔可夫决策模型等优化模型的建模方法；分析了该类模型在应用中存在的不足，并展望了其发展前景。     

视情维修技术及其在飞机维修中的应用

本文描述了视情维修技术的基本思想及其相关的各项关键技术,对视情维修关键技术需求与目前该领域最新技术发展水平进行了对比分析,总结了制约视情维修技术在飞机维修中全面应用的环节,介绍了视情维修相关的预测与健康管理系统在飞机维修中的应用实例。     

电气设备实施状态维修决策方法的探讨

在电气设备的使用中常常会出现一些故障,为了保证电气设备的正常运转,因而实施维修必不可少。实施维修能够降低设备的故障率,提高电气设备的使用效率,保证相关用电设备的正常运行。在当前形势下,原有的电气设备维护模式技术已严重落后,不能满足现代化需求,一种新的检修方式——状态维修出现。文章系统介绍了状态维修的优点,分析了这种维修方式在空调压缩机等现代家用电器设备中的应用,同时指明了状态维修的一般决策过程和决策方法。     

一种新的小波阈值函数及其在振动信号去噪分析中的应用

摘要：研究一种新的小波收缩阈值函数用于信号的去噪分析,对比分析了硬阈值、软阈值和新收缩阈值函数的优缺点,给出了收缩阈值函数法中的阈值计算详细过程,基于虚拟仪器LabVIEW构建检测齿轮箱系统的振动与噪音检测系统,在MATLAB平台上利用收缩阈值方法开发了对齿轮箱振动和噪声信号进行去噪处理的软件,试验数据的分析表明：基于新的小波阈值函数的信号降噪分析方法去噪效果明显,且保留了原始信号的细节特征,是一种较传统经典去噪手段更为优越的方法,具有较高的实用价值。
     

完善舰船装备维修体制 提高预防性维修水平

分析了海军装备目前的维修现状以及存在的问题,论述了实行视情维修的重要性和必要性,提出了建立以视情维修为主的预防性维修大纲的原则。     

基于RCM的港口机械关键设备的维修决策

以港口起重机械为对象,在为振华港机建立的以可靠性为中心的维修决策流程框架下,对所涉及的维修方式进行研究优化.结合故障率调整因子,探索了按有效度最大的原则确定港口机械的最佳预防维修周期的算法;基于P-F间隔,提出了基于RCM的状态维修检测周期算法.最后,开发了可靠性维护系统,通过程序开发,实现各项功能模块.为企业的设备维护提供科学的决策支持.     

装甲装备器材保养包装平台的构建

装甲装备器材保养包装是器材储备供应体系的重要环节,是保证器材质量完好、提高器材保障能力的重要技术措施。以保养包装新模式为理论指导,研制了自动化保养设备及系列保养包装材料,制定了保养包装规范,开发了质量管理信息系统,在此基础上,设计并建设了装甲装备器材保养包装平台,全面提升了装甲装备器材保养包装质量与效能。     

A Case Study of GAMM (Graphical Analysis for Maintenance Management) Applied to Water Pumps in a Sewage Treatment Plant,Chile

This article presents a case for practical application of the GAMM method, which has been developed and published by the authors. The GAMM method supports decision making in the overall maintenance management, through the visualization and graphical analysis of data. In addition, it allows for the identification of anomalous behavior in the equipment analyzed, whether derived from its own operations, maintenance activities, improper use of equipment or even as a result of design errors in the equipment itself. As a basis for analysis, the GAMM method uses a nonparametric estimator of the reliability function using all historical data or, alternatively, part of the history, allowing it to perform an analysis even with limited available data. In the case study presented, the GAMM method has been used to analyze two existing equipment parts in a sewage treatment plant located in Chile. The equipment analyzed were two progressive cavity pumps (BCP) which are part of the industrial process in the treatment plant. Both pumps are the same make and model and subjected to the same maintenance program, but one had a higher failure rate compared to its counterpart. The application of the GAMM method has identified deficiencies in each of the pumps being studied, thus improving decision‐making and problem‐solving process related to the maintenance of the pumps. RCM methodology was used to draft a new maintenance plan. Copyright © 2013 John Wiley & Sons, Ltd.     

基于DS–PCA模型的包装设备滚动轴承故障诊断方法研究

目的 针对包装设备长期连续工作的工作特性,对其滚动轴承的常见失效故障进行分析,提出一种基于DS–PCA模型的滚动轴承故障诊断方法,构建滚动轴承DS–PCA故障诊断模型,实现包装设备中滚动轴承的在线故障诊断。方法 先运用DS证据理论对采集到的滚动轴承径向振动数据和轴向振动数据进行融合,使得信息具有整体完备性,同时对采集信号进行决策规则下的去噪处理,剔除干扰噪声信息;然后利用主成分分析法(PCA)将融合后的振动信号数据进行Q与T²的统计量计算,并通过对故障轴承振动信号的Q与T²统计量计算,确立故障时的经验阈值;最后,依据实时统计量与经验阈值对比,判断滚动轴承是否发生故障。结果 通过对西储大学公开的滚动轴承试验数据分析计算,得到滚动轴承故障诊断准确率达到94%。结论 该方法满足包装设备故障诊断的要求,其应用将有利于提升包装企业的生产质量和效率。     

The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making

This article proposes a logical support tool for maintenance management decision making. This tool is called the Graphical Analysis for Maintenance Management (GAMM), a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short–medium term) and potential investments (medium–long term). The method allows an easy visualization of parameters, such as the number of corrective actions between preventive maintenance, the accumulation of failures in short periods of time, and the duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own function, maintenance activities, misuse, or even equipment designs errors. In this method, we used a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data. Copyright © 2012 John Wiley & Sons, Ltd.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 John Wiley & Sons, Ltd.     

Graphical Analysis for Operation Management: A Graphical Method to Support Operation Decision Making

This paper proposes a graphical method to easy decision‐making in industrial plants operations. The proposed tool ‘Graphical Analysis for Operation Management (GAOM) method’ allows to visualizing and analyzing production‐related parameters, integrating assets/systems maintenance aspects. This integration is based on the Total Productive Maintenance model, using its quantitative management techniques for optimal decision‐making in day‐to‐day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability, repair time, cumulative production, or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance, or operational intervention) and production level can be developed. Graphical Analysis for Operation Management (GAOM) integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision‐making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the Graphical Analysis for Maintenance Management method, published by the authors (LB, AC, and PV) in 2012. Copyright © 2015 John Wiley & Sons, Ltd.     

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

The performance of a production system depends on the breakdown-free operation of equipment and processes. Maintenance and quality control play an important role in achieving this goal. In addition to deteriorating with time, equipment may experience a quality shift (i.e. process moves to out-of-control state), which is characterised by a higher rejection rate and a higher tendency to fail. This paper develops an integrated model for joint optimisation of preventive maintenance interval and control parameters incorporating the Taguchi loss function. We consider two types of maintenance policies: minimal corrective maintenance that maintains the state of the equipment without affecting the age and imperfect preventive maintenance that upgrades the equipment in between ‘as good as new’ and ‘as bad as old’ condition. The proposed model enables the determination of the optimal value of each of the four decision variables, i.e. sample size (n), sample frequency (h), control limit coefficient (k), and preventive maintenance interval (t _PM) that minimises the expected total cost of the integration per unit time. A numerical example is presented to demonstrate the effect of the cost parameters on the joint economic design of preventive maintenance and process quality control policy. The sensitivity of the various parameters is also examined.     

基于故障状态演化的租赁设备定周期多维护策略研究

为同时保证设备承租方对租赁设备的可用度,以及优化设备出租方的设备维护成本,提出了基于故障状态的定周期检测的多维护方式策略。首先,对租赁设备进行定周期检测,基于设备故障率来选择相应的维护策略,采取役龄回退的方式描述采取不同的维护策略后设备状态的恢复情况。其次,综合考虑周期维护成本、小修成本、惩罚成本和租赁延迟成本,建立了以出租方成本最低为目标的多维护策略优化模型。通过数学建模和数理统计方法,利用MATLAB仿真进行算例分析,将其与定周期单一预防性维护策略进行对比,证明了对租赁设备进行定周期多策略维护,其维护效果较单一预防性维护有较大的提升。     

基于寿命周期成本分析的产品延伸保修决策优化模型

随着用户对产品寿命周期成本的日益重视,延伸保修已成为生产商新的关注点。延伸保修对产品维修使用费用以及市场竞争力有很大影响,为得到产品延伸保修决策优化方案以降低寿命周期成本,在考虑预防性维修策略的基础上,分析了生产商延伸保修定价策略,构建出一种以用户在产品使用寿命期内单位时间总维修成本最小为目标的延伸保修决策优化模型,并提出了求解模型的网格搜索(grid search)算法。通过引入算例,借助Matlab仿真软件实现网格搜索算法,在保证生产商利益的基础上,有效的降低了产品单位时间总维修成本,延长了产品使用寿命周期长度,由此得到了用户最优延伸保修决策方案,验证了模型和算法的合理性和有效性。