Effective machine maintenance policy is a critical element of a smooth running manufacturing system. This paper evaluates a multiple-recipe predictive maintenance problem modeled using a M/G/1 queueing system. A numerical study is performed on an optimal predictive maintenance policy. A simulated job-based maintenance policy is used as a baseline for the optimal policy. We investigate the effects of varying degradation rates, holding costs, preventive maintenance times, and preventive maintenance costs. We also examine a two-recipe problem. / text

Same types of systems with the same age usually have different amounts of cumulated usage. These systems when in operation usually have different performance and effectiveness. In this case the existing models of the univariate measures of system effectiveness are inadequate and incomplete. For example, the univariate availability measures for these same-aged systems are all the same even though with different amounts of usage. This is the motivation for this research to pursue a bivariate approach in reliability and maintenance modeling. This research presents a framework for bivariate modeling of a single-unit system. Five key efforts are identified and organized as: (i) bivariate failure modeling, (ii) bivariate renewal modeling, (iii) bivariate corrective maintenance (CM) modeling, (iv) bivariate preventive maintenance (PM) modeling, and (v) bivariate availability modeling. The results provide a foundation for further study of bivariate and multivariate models. For bivariate failure modeling, several bivariate failure models are constructed to represent the possible correlation structures of the two system aging variables, time and usage. The behavior of these models is examined under various correlation structures. The developed models are used to analyze example maintenance problems. Models for bivariate renewal, bivariate CM, and bivariate PM are derived based on the constructed bivariate failure models and the developed bivariate renewal theory. For bivariate CM modeling, corrective maintenance is modeled as an alternating bivariate renewal process or simply an ordinary bivariate renewal process. For bivariate PM modeling, PM models are examined under a bivariate age replacement preventive maintenance policy. The Laplace transforms of the renewal functions (and densities) for these models are obtained. Definitions for bivariate availability functions are developed. Based on the derived CM and PM models, the Laplace transforms for their corresponding bivariate availability models are constructed. The idea of the quality of availability measure is also defined in terms of bivariate availability models. The most significant observation is that this framework provides a new way to study the reliability and maintenance of equipment for which univariate measures are incomplete. Therefore, a new area of reliability research is identified. The definitions offered may be modified and the approach to model formulation presented may be used to define other models. / Ph. D.

Industrial operational environments are stochastic and can have complex system dynamics which introduce multiple levels of uncertainty. This uncertainty leads to sub-optimal decision making and resource allocation. Digitalisation and automation of production equipment and the maintenance environment enable predictive maintenance, meaning that equipment can be stopped for maintenance at the optimal time. Resource constraints in maintenance capacity could however result in further undesired downtime if maintenance cannot be performed when scheduled. In this dissertation the applicability of using a Multi-Agent Deep Reinforcement Learning based approach for decision making is investigated to determine the optimal maintenance scheduling policy in a fleet of assets where there are maintenance resource constraints. By considering the underlying system dynamics of maintenance capacity, as well as the health state of individual assets, a near-optimal decision making policy is found that increases equipment availability while also maximising maintenance capacity. The implemented solution is compared to a run-to-failure corrective maintenance strategy, a constant interval preventive maintenance strategy and a condition based predictive maintenance strategy. The proposed approach outperformed traditional maintenance strategies across several asset and operational maintenance performance metrics. It is concluded that Deep Reinforcement Learning based decision making for asset health management and resource allocation is more effective than human based decision making. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

Syftet med denna uppsats är att undersöka en komponents    optimala utbyts" tid med hänsyn till kostnad och risk,    och föreslå schemalagda underhåll, med hjälp av statistiska metoder.    Genom att använda statistiska verktyg och historiska data, kan    en komponents samt systemets brister predikteras. När    forskaren vet hur ett system beter sig, kan dess fördelar    exploateras och tas till vara på. Schemaläggning av    förebyggande service, kostnads prognoser samt    uppskattning av förlängda garantier är möjliga fördelar som    kan nyttjas av denna rapport. Detta medför en högre    tillgänglighet och förbättrat rykte hos kund.    Tillförlitligthet teori är en viktigt del av    Total Quality Management, TQM, som säkerhetsställer kvalité.    I denna uppsats jämförs, förklaras och verifieras 2 kända ARP,    och strategin att ersätta endast då komponenten går sönder i en fallstudie.    Denna uppsats indikerar att en ARP med ändlig horisont ger mer optimalta resultat än    en ARP med oändlig horisont eller då ingen utbytespolicy används.    Barlow \& Proschan visade detta redan 1962.    I denna uppsats påvisas att ARP-teorier kan minska omkostnader och stilleståndstid    samt öka tillgängligheten. / The purpose of this thesis is to examine when a part's optimal   replacement time occurs in terms of risk and cost, and provide maintenance plans   accordingly using statistical methods.   With the use of statistical tools and historical data,   the failures of components as well as the system can be predicted.   Once the researcher knows how the system behaves, he/she can reveal the gains that   can be made. Scheduling of preventive maintenance, improved warranty cost   forecasts and estimation of lengthened warranty costs are   plausible benefits from this report. This will further result   in higher availability and improved reputation among clients.   Reliability theory is an important part of Total Quality Management (TQM),   ensuring good quality.   This thesis will compare the differences between two known age replacement policies (ARP),   and with the strategy of replacing only on failures in a real case-study.   This thesis indicates that an ARP with finite horizon yields   a more optimal solution than an ARP with infinite horizon as well as using no replacement policy at all.   Barlow & Proschan established this as far back as 1962.   With the aid of ARP theories it has been shown in this thesis that lowering costs is possible   and in the progress lower downtime which increases availability.

<p>Syftet med denna uppsats är att undersöka en komponents    optimala utbyts" tid med hänsyn till kostnad och risk,    och föreslå schemalagda underhåll, med hjälp av statistiska metoder.    Genom att använda statistiska verktyg och historiska data, kan    en komponents samt systemets brister predikteras. När    forskaren vet hur ett system beter sig, kan dess fördelar    exploateras och tas till vara på. Schemaläggning av    förebyggande service, kostnads prognoser samt    uppskattning av förlängda garantier är möjliga fördelar som    kan nyttjas av denna rapport. Detta medför en högre    tillgänglighet och förbättrat rykte hos kund.    Tillförlitligthet teori är en viktigt del av    Total Quality Management, TQM, som säkerhetsställer kvalité.    I denna uppsats jämförs, förklaras och verifieras 2 kända ARP,    och strategin att ersätta endast då komponenten går sönder i en fallstudie.    Denna uppsats indikerar att en ARP med ändlig horisont ger mer optimalta resultat än    en ARP med oändlig horisont eller då ingen utbytespolicy används.    Barlow \& Proschan visade detta redan 1962.    I denna uppsats påvisas att ARP-teorier kan minska omkostnader och stilleståndstid    samt öka tillgängligheten.</p> / <p>The purpose of this thesis is to examine when a part's optimal   replacement time occurs in terms of risk and cost, and provide maintenance plans   accordingly using statistical methods.   With the use of statistical tools and historical data,   the failures of components as well as the system can be predicted.   Once the researcher knows how the system behaves, he/she can reveal the gains that   can be made. Scheduling of preventive maintenance, improved warranty cost   forecasts and estimation of lengthened warranty costs are   plausible benefits from this report. This will further result   in higher availability and improved reputation among clients.   Reliability theory is an important part of Total Quality Management (TQM),   ensuring good quality.   This thesis will compare the differences between two known age replacement policies (ARP),   and with the strategy of replacing only on failures in a real case-study.   This thesis indicates that an ARP with finite horizon yields   a more optimal solution than an ARP with infinite horizon as well as using no replacement policy at all.   Barlow & Proschan established this as far back as 1962.   With the aid of ARP theories it has been shown in this thesis that lowering costs is possible   and in the progress lower downtime which increases availability.</p>

Dans la littérature, plusieurs travaux ont été développés autour de la modélisation, l’analyse et la mise en place de politiques de maintenance pour les équipements sujets à des défaillances aléatoires. Ces travaux occultent souvent les réalités industrielles par des hypothèses telles que la connaissance a priori des distributions paramétriques des durées de vie et l’homogénéité des conditions d’exploitation des équipements. Ces hypothèses sont restrictives et constituent une source de biais parce qu’elles conditionnent l’analyse statistique des politiques de maintenance. Dans ce présent travail de thèse, de telles hypothèses sont relaxées pour permettre la prise en compte et la mise en valeurs des informations dérivant directement des données de durées vie issues de l’exploitation de l’équipement et ce sans passer par un modèle paramétrique intermédiaire. L’objectif de ce travail de thèse consiste alors en le développement de modèles statistiques et d’outils efficaces pour l’analyse des politiques de maintenance basées sur les données de durées de vie hétérogènes. Nous proposons en effet une démarche complète d’analyse de stratégies de maintenance en partant des données de durées de vie jusqu’à l’obtention des politiques optimales de maintenance en passant par une phase d’estimation des lois de probabilité. Les politiques de maintenance considérées sont appliques à des équipements usagés évoluant dans des environnements d’exploitation distingués par leur niveau de sévérité. Dans ce contexte, un modèle mathématique est proposé permettant d’évaluer et d’analyser théoriquement les coûts unitaires d’une stratégie de maintenance particulière dite de type âge. Cette analyse a permis d’établir les conditions nécessaires et suffisantes garantissant un âge optimal de remplacement préventif de l’équipement. Les coûts unitaires de maintenance sont complètement estimés par la méthode du Noyau de Parzen. Cette méthode d’estimation est non-paramétrique et définie par une fonction noyau et un paramètre de lissage. Il est également montré, dans nos travaux de recherche, que cet estimateur garantit une faible propagation des erreurs induites par le paramètre de lissage. Les résultats obtenus par la méthode du Noyau de Parzen sont proches des valeurs théoriques avec un faible coefficient de variation. Des extensions de la première politique de maintenance sont également proposées et étudiées. Ce travail de thèse s’achève par la proposition d’une approche permettant de mesurer et d’analyser le risque induit par le report d’une maintenance préventive. Ce risque est analysé à travers une fonction risque proposée / In the reliability literature, several researches works have been developed to deal with modeling, analysis and implementation of maintenance policies for equipments subject to random failures. The majority of these works are based on common assumptions among which the distribution function of the equipment lifetimes is assumed to be known. Furthermore, the equipment is assumed to experience only one operating environment. Such assumptions are indeed restrictive and may introduce a bias in the statistical analysis of the distribution function of the equipment lifetimes which in turn impacts optimization of maintenance policies. In the present research work, these two particular assumptions are relaxed. This relaxation allows to take into account of information related to conditions where the equipment is being operating and to focus on the statistical analysis of maintenance policies without using an intermediate parametric lifetimes distribution. The objective of this thesis consists then on the development of efficient statistical models and tools for managing the maintenance of equipments whose lifetimes distribution is unknown and defined through the heterogeneous lifetimes data. Indeed, this thesis proposes a framework for maintenance strategies determination, from lifetimes data acquisition toward the computation of optimal maintenance policies. The maintenance policies considered are assumed to be performed on used equipments. These later are conduct to experience their missions within different environments each of which is characterized by a degree of severity. In this context, a first mathematical model is proposed to evaluate costs induced by maintenance strategies. The analysis of these costs helps to establish the necessary and sufficient conditions to ensure the existence of an optimal age to perform the preventive maintenance. The maintenance costs are fully estimated by using the Kernel method. This estimation method is non-parametric and defined by two parameters, namely the kernel function and the smoothing parameter. The variability of maintenance costs estimator is deeply analyzed according to the smoothing parameter of Kernel method. From these analyses, it is shown that Kernel estimator method ensures a weak propagation of the errors due to the computation of smoothing parameter. In addition, several simulations are made to estimate the optimal replacement age. These simulations figure out that the numerical results from the Kernel method are close to the theoretical values with a weak coefficient of variation. Two probabilistic extensions of the first mathematical model are proposed and theoretically discussed. To deal with the problem of delayed preventive maintenance, an approach is proposed and discussed. The proposed approach allows evaluating the risk that could induce the delay taken to perform a preventive maintenance at the required optimal date. This approach is based on risk analysis conduct on the basis of a proposed risk function

Thesis (MScEng)--Stellenbosch University / ENGLISH ABSTRACT: The decline of the passenger rail transport system of South Africa over the past two decades has left the passenger rail industry in a difficult position. The most significant impact has been the deterioration of the physical assets. Due to the renewed focus by government on passenger rail transport, the need for improving the physical asset management has been recognised. Physical asset management manifests itself through the application of strategies. The need for new and/or updated strategies was identified and summarily examined. Through the initial literature study, it was found that strategies are founded on the specific maintenance policy of an organisation. The application of the new/updated strategies was intended to take place at Metrorail. An investigation at Metrorail revealed the lack of any significant policy that is required to develop any new strategies. This discovery led to a shift in focus from the development of new strategies to the development of a physical asset management policy. A generic policy statement called Requirement-­‐based Asset Management (RAM) was developed, with its primary focus being the conducting of maintenance activities based on the requirements of the organisation, the employees, the asset and the customer. In order to evaluate the suitability of RAM, a strategic roadmap was developed based on the policy statement and validated in three areas of Metrorail. These three areas were the wheel set maintenance system, the Top 7 fault evaluation procedure and the scheduled maintenance cycle of the train sets. The application procedure concluded that the roadmap and thus by deduction RAM are suitable for the Metrorail environment. RAM can be used to develop/improve an organisation’s physical asset management policy. / AFRIKAANSE OPSOMMING: Die agteruitgang van die vervoerspoorwegstelsel vir passasiers gedurende die afgelope twee dekades in Suid-­‐Afrika het hierdie bedryf in ‘n moeilike posisie geplaas. Die mees beduidende impak van hierdie verwaarlosing is die agteruitgang van die instandhouding van fisiese bates. Die regering se hernuwe fokus op die vervoer van passasiers per spoor het gelei tot die herkenning van die behoefte aan verbeterde bestuur van fisiese bates. Die bestuur van fisiese bates word gemanifesteer deur die toepassing van strategieë. ‘n Behoefte aan nuwe en/of opgedateerde strategieë is geïdentifiseer en nagevors. Die aanvanklike literatuurstudie het bevind dat strategieë op ‘n organisasie se spesifieke instandhoudingsbeleid gebaseer is en die toepassing van hierdie nuwe en/of opgedateerde strategieë is beplan by Metrorail. ‘n Gebrek aan ‘n noemenswaardige beleid wat vereis word vir die ontwikkeling van nuwe strategieë is by Metrorail gevind. Hierdie bevinding het ‘n fokusverskuiwing tot gevolg gehad – van die ontwikkeling van nuwe strategieë na die ontwikkeling van ‘n bestuursbeleid vir fisiese bates. ‘n Generiese beleidsverklaring genaamd “Requirement-­‐based Asset Management” (RAM), met die primêre fokus op instandhoudingsaktiwiteite, is ontwikkel en is gebaseer op die behoeftes van die organisasie, die werknemers, bates en kliënte. ‘n Strategiese metodologie wat op die beleidsverklaring gebaseer is, is ontwikkel om die geskiktheid van die RAM te evalueer en is dit in drie areas van Metrorail gevalideer. Hierdie drie areas sluit in die instandhoudingstelsel vir wielstelle, die prosedures betrokke by die evaluasie van die sewe mees beduidende foute, en die geskeduleerde instandhoudingsiklus van die treinstelle. Deur die toepassingsprosedure is die gevolgtrekking gemaak dat die metodologie, en gevolglik die RAM, geskik is vir die Metrorail-­‐omgewing. Die RAM kan dus gebruik word vir die ontwikkeling en/of verbetering van ‘n organisasie se bestuursbeleid vir fisiese bates.

Student number: 0200613W Faculty of Engineering and the Built Environment Master of Property Development and Management. / This paper is a report on the research undertaken to evaluate the implementation of the Urban Renewal strategies that the City of Johannesburg adopted for the CBD renewal through the Blue IQ. The study was restricted to the views expressed by the general community, business community, Johannesburg Development Agency (JDA). Johannesburg Housing Company (JHC), Blue IQ, Gauteng Development Agency and Gauteng Economic Development Agency (GEDA), which are the main role players in the CBD renewal effort. There was administered questioner to the members of the Business and general communities that were randomly picked using the fish bowl method. Interviews were conducted with the senior executive staff of JDA. Blue IQ, JHC. Statistics derived from the above company s websites was used as well. The results of the study show that the renewal strategy has been largely successful in so far as a number of factors, which have contributed to the CBD decay such as poor infrastructure and slumps. There have also been considerable efforts to address acute shortage of parking space by private partners such as financial institutions. Although crime has decreased it is still posing a serious challenge as most people still consider the CBD high risk. However there is still a room for improvement, which includes: the enhancement of safety and security, the infrastructure maintenance policy and the enforcement of the municipality by laws and town planning scheme. Overall the strategies have also improved the economic performance of the city significantly though unemployment still remain high with the ever increasing number of people coming to seek opportunities.

In today's competitive environment, the importance of continuous production, quality improvement, and fast delivery has forced production and delivery processes to become highly reliable. Keeping equipment in good condition through maintenance activities can ensure a more reliable system. However, maintenance leads to temporary reduction in capacity that could otherwise be utilized for production. Therefore, the coordination of maintenance and production is important to guarantee good system performance. The central thesis of this dissertation is that integrating maintenance and production decisions increases efficiency by ensuring high quality production, effective resource utilization, and on-time deliveries. Firstly, we study the problem of integrated maintenance and production planning where machines are preventively maintained in the context of a periodic review production system with uncertain yield. Our goal is to provide insight into the optimal maintenance policy, increasing the number of finished products. Specifically, we prove the conditions that guarantee the optimal maintenance policy has a threshold type. Secondly, we address the problem of integrated maintenance planning and production scheduling where machines are correctively maintained in the context of a dynamic aircraft repair shop. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter periods. Our results show that the approach that uses logic-based Benders decomposition to solve the static sub-problems, schedules over longer horizon, and quickly adjusts the schedule increases the utilization of aircraft in the long term. Finally, we tackle the problem of integrated maintenance planning and production scheduling where machines are preventively maintained in the context of a multi-machine production system. Depending on the deterioration process of machines, we design decomposed techniques that deal with the stochastic and combinatorial challenges in different, coupled stages. Our results demonstrate that the integrated approaches decrease the total maintenance and lost production cost, maximizing the on-time deliveries. We also prove sufficient conditions that guarantee the monotonicity of the optimal maintenance policy in both machine state and the number of customer orders. Within these three contexts, this dissertation demonstrates that the integrated maintenance and production decision-making increases the process efficiency to produce high quality products in a timely manner.