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 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.     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization

Maintenance concerns impact systems in every industry and effective maintenance policies are important tools. We present a methodology for maintenance decision making for deteriorating systems under conditions of uncertainty that integrates statistical quality control (SQC) and partially observable Markov decision processes (POMDPs). We use simulation to develop realistic maintenance policies for real‐world environments. Specifically, we use SQC techniques to sample and represent real‐world systems. These techniques help define the observation distributions and structure for a POMDP. We propose a simulation methodology for integrating SQC and POMDPs in order to develop and valuate optimal maintenance policies as a function of process characteristics, system operating and maintenance costs. A two‐state machine replacement problem is used as an example of how the method can be applied. A simulation program developed using Visual Basic for Excel yields results on the optimal probability threshold and on the accuracy of the decisions as a function of the initial belief about the condition of the machine. This work lays a foundation for future research that will help bring maintenance decision models into practice. Copyright © 2005 John Wiley & Sons, Ltd.     

Integrated maintenance planning and production scheduling with Markovian deteriorating machine conditions

In many industries, production capacity diminishes as machine conditions deteriorate. Maintenance operations improve machine conditions, but also occupy potential production time, possibly delaying the customer orders. Therefore, one challenge is to determine the joint maintenance and production schedule to minimize the combined costs of maintenance and lost production over the long term. In this paper, we address the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system over multiple periods. Assuming that at the beginning of each period the demand becomes known and machine conditions are observable, we formulate a Markov decision process model to determine the maintenance plan and develop sufficient conditions guaranteeing its monotonicity in both machine condition and demand. We then formulate an integer programming model to find the maintenance and the production schedule in each period. Our computational results show that exploiting online condition monitoring information in maintenance and production decisions leads to 21% cost savings on average compared to a greedy heuristic and that the benefit of incorporating long-term information in making short-term decisions is highest in industries with medium failure rates.     

ANP/RPN: a multi criteria evaluation of the Risk Priority Number

This paper presents an advanced version of the failure mode effects and criticality analysis (FMECA), whose capabilities are enhanced; in that the criticality assessment takes into account possible interactions among the principal causes of failure. This is obtained by integrating FMECA and Analytic Network Process, a multi‐criteria decision making technique. Severity, Occurrence and Detectability are split into sub‐criteria and arranged in a hybrid (hierarchy/network) decision‐structure that, at the lowest level, contains the causes of failure. Starting from this decision‐structure, the Risk Priority Number is computed making pairwise comparisons, so that qualitative judgements and reliable quantitative data can be easily included in the analysis, without using vague and unreliable linguistic conversion tables. Pairwise comparison also facilitates the effort of the design/maintenance team, since it is easier to place comparative rather than absolute judgments, to quantify the importance of the causes of failure. In order to clarify and to make evident the rational of the final results, a graphical tool, similar to the House of Quality, is also presented. At the end of the paper, a case study, which confirms the quality of the approach and shows its capability to perform robust and comprehensive criticality analyses, is reported. Copyright © 2011 John Wiley and Sons Ltd.     

An evaluation system of the setting up of predictive maintenance programmes

Predictive Maintenance can provide an increase in safety, quality and availability in industrial plants. However, the setting up of a Predictive Maintenance Programme is a strategic decision that until now has lacked analysis of questions related to its setting up, management and control. In this paper, an evaluation system is proposed that carries out the decision making in relation to the feasibility of the setting up. The evaluation system uses a combination of tools belonging to operational research such as: Analytic Hierarchy Process, decision rules and Bayesian tools. This system is a help tool available to the managers of Predictive Maintenance Programmes which can both increase the number of Predictive Maintenance Programmes set up and avoid the failure of these programmes. The Evaluation System has been tested in a petrochemical plant and in a food industry.     

面向供应链的全员生产维护管理模式

提出了一种适应于协同业务环境的设备维护管理新模式，着重论述了以快速反应战略、供应链思想和全员生产维护制所构建的体系结构，并对其独特的设计逻辑进行分析与实现，旨在强调在变革企业管理模式及优化企业资源配置的基础上，面向供应链的全员生产维护管理在提高企业竞争优势中的战略意义。     

具有协变量的高斯图模型的结构学习

图模型是一种研究变量之间相依关系的重要工具．除了节点变量外,数据常常包括协变量而且可能影响网络结构．然而现有关于图模型的工作大多仅考虑节点变量．本文基于图模型研究具有协变量的网络结构特征学习问题,在稀疏正则化的框架下,通过假设变量之间的条件独立为线性关系,建立具有协变量信息的稀疏高斯图模型,估计网络结构特征．所得结果具有实际解释性且易于求解,我们利用坐标下降法求解模型,通过实验说明含协变量比无协变量的效果更好,从而说明本文模型的高效性和实用性．     

我国工业维护管理的现状与发展

本文介绍了工业维护管理（Industrial Maintenance Management，简称IMM）在我国的发展历程和取得的经验，分析了我国制造业应用IMM中存在的问题，指出了我国IMM发展的方向，即发展基于集成化的IMM模式，走集中维护、分散维护和专业维护相结合的道路，以及IMM与先进制造模式相结合。     

Plant-level maintenance decision support system for throughput improvement

In the current global scenario of extreme competition, factors such as productivity, availability, quality and cost of operations play a vital role in the success of a company. A critical component relating to all of the above is maintenance. The conventional maintenance decision support systems have primarily focused on maximising the gains of a single machine system. However, a real life application usually consists of multiple machines, and the operational level decisions are more complex. In this paper, an on line plant-level maintenance decision support system (PMDSS) is developed by combining the short term and long term decision making process to improve the overall system performance while continuously attempting to maximise immediate profits in the short term. The PMDSS works towards two basic aims: (1) unplanned downtime reduction by predicting the remaining useful life of the machines, and (2) efficient utilisation of the finite maintenance and production resources through identifying the throughput-critical machines. The benefits of this approach are presented by considering an industrial case study of an automotive assembly line. The results obtained using this PMDSS approach shows a big throughput improvement as compared to the conventional maintenance policies.     

A graphical approach for confidence limits of optimal preventive maintenance cycles

Facilities management (FM) is the management of infrastructure resources and services to support and sustain the operational strategy of an organization over time. Maintenance is often the business process that has not been optimized and is considered as a liability of business operations. Therefore, extensive studies have been done to determine the optimal replacement interval for irreparable parts of repairable systems where typically the time between failures is characterized by lifetime distribution in which the parameters are estimated from failure data. As a result, the optimal preventive maintenance (PM) interval computed is exposed to sampling risk as the repair cost and failure data used for estimation are typically highly censored due to issues related to data collection and unobserved failures. In this paper, we present a graphical approach to obtain the confidence interval for the optimal PM interval that resulted from sampling variations parameter estimates. The proposed methodology is applied in the context of FM as a strategy for opportunistic replacement and for the purpose of validating the cost components in maintenance. Copyright © 2008 John Wiley & Sons, Ltd.     

Sicherer und wirtschaftlicher Betrieb von Kraftwerken – Ergebnis der Forschung im Bereich Werkstoffe,Auslegung und Instandhaltung

Safe and Economic Operation of Power Plants – Research Results in the Field of Materials, Design and Maintenance Components of power plants which undergo high temperatures are subjected to complex loading situations. The requirements on the used materials result from the special operation conditions of the plants and have to be adjusted to the steadily growing requirements on higher efficiency of the complete power plant as well as to those of safe and economic operation. The expenses for control and downtimes are directly connected with economic efficiency and availability. However, in case of new procedures or components it is not possible to revert to the existing know‐how. Different failure mechanisms than known before can occur. The same goes for the load situation. Therefore the knowledge base has to be extended to in‐advance or even parallely running scientific examinations that life assessment and maintenance strategies can be applied which guarantee the operational reliability and the efficiency of the plant. The main emphasis of these F&E works has to be put on condition monitoring based on actual operational data, the standard materials’ and component’s behaviour (deformation, damage and failure behaviour) in connection with design of components and the related material laws. In the framework of applied AVIF projects, following problems are handled:. – qualification of materials by determinating parameters related to practise. – optimal design of components by making available material laws and numerical tools. – economic manufacturing of components by qualifying processing methods such as welding. – The results can be transferred to concepts for safe and economic operation of power plants, especially for newly introduced materials for which there is no operational experience available.     

基于灰信息挖掘的视情维修决策方法研究

针对列装新设备缺乏状态劣化信息和维修阈值难以进行视情维修决策的问题,基于新旧设备故障率变化趋势的一致性,定义改进性系数表示基本故障率的变化不同,得到新型设备的故障率函数,结合回归支持向量机拟合新型设备风险度函数曲线,得到新型设备的维修阈值,进而建立新型设备的视情维修决策模型,最后以船用柴油机监测数据进行了实例验证。结果表明,该方法能有效挖掘旧型柴油机的历史数据信息,充分利用新旧柴油机之间的内在联系,为灰信息条件下新设备的视情维修决策提供了新途径。     

Study of Enterprise Equipment Maintenance Management Integrated Information System Based on B/S

1IntroductionTheaimofequipmentmanagementistopursuemaximumequipmentintegratedefficiencyandminimumequipmentlifetimecyclecosts.Uptonow,thedevelopmentofequipmentmaintenancestylehasundergoneseveralstagesofbreakdownmaintenance,preventivemaintenanceandcondition-basedmaintenance.Accordingly,computeraidedequipmentmanagementinformationsystemhasgonethroughthefollowingstages:1)Mostlyusedbymanagementdepartment,itwasmainlyaboutECPEquipmentControlProcessing)orbreakdownmaintenancemanagementsystemthatwasmai…     

A Functional Inspection Model for the Immeasurable Potential Failure State

Functional inspection is a type of preventive maintenance of Reliability Centered Maintenance(RCM). We, in this paper, establish a functional inspection model(FIM)——the cost model and the availability model for the immeasurable potential failure state based on the delay time concept. This model can be used to determine the appropriate Functional Inspection Interval(FII) to achieve the goal of specific cost and availability and to assist in maintenance decision making.     

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

Development and Application of a Decision Support System for Human Reliability Assessment – A Case Study of an Electric Power Company

This paper describes the human reliability assessment using absolute probability judgment (APJ) method in the Electric Power Company of Serbia (EPS). Taking into account conventional APJ approach, a new decision support tool EPS‐APJ was developed. The EPS‐APJ tool is intended for use by a group of experts for human error probability assessment. The basic goal of the tool is to facilitate the process of decision‐making by experts, in the complex environment of the electric power companies. The usefulness of this new tool is approved in a case study of use of the EPS‐APJ for the purpose of the analysis of a repair intervention on a 10/0.4‐kV steel lattice tower ‘Nogaje’, in the EPS. For the purpose of this study, a database on work‐related injuries, accidents, and critical interventions occurred over a 10‐year period was created. The research comprised analysis of 1074 workplaces, with the total of 3997 employees. The case study performed at the EPS has confirmed that the EPS‐APJ tool for quantification of human errors is highly applicable, comprehensive, and simple to use. Comparison of the results obtained by application of the EPS‐APJ tool versus the basic APJ method has confirmed the accuracy of the decision support tool, and significant speed advantage in conducting an expert evaluation. Copyright © 2015 John Wiley & Sons, Ltd.     

Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.     

Equity Breakeven Point: A Graphical and Tabulation Tool for Engineering Managers

Abstract

This article presents a graphical and tabulation framework for the application of equity breakeven point (EBP) for financial management of engineering projects. EBP was previously developed as a computational tool for the analysis of mortgage payments. This article presents a user-friendly framework for visualizing and assessing the utility of EPB in engineering projects. The proposed graphical and tabulation framework extends the application of EPB to general capital investments, which are part of engineering projects. Engineering managers and other practitioners can utilize the tabulation of EBP to assess what-if options in major financial transactions. The mathematical derivation of the EPB indicates the time when the unpaid balance on a capital investment is equal to the cumulative equity in the investment, thereby providing a-priori insight into how long it might take to retire an investment loan on the strength of the accrued equity. This type of graphical visualization is useful for negotiating the terms of acquiring and/or managing large engineering projects.