Maximum asset performance is one of the major goals for electric power distribution system operators (DSOs). To reach this goal minimal life cycle cost and maintenance optimization become crucial while meeting demands from customers and regulators. One of the fundamental objectives is therefore to relate maintenance and reliability in an efficient and effective way. Furthermore, this necessitates the determination of the optimal balance between pre¬ventive and corrective maintenance, which is the main problem addressed in the thesis. The balance between preventive and corrective maintenance is approached as a multiobjective optimization problem, with the customer interruption costs on one hand and the maintenance budget of the DSO on the other. Solutions are obtained with meta-heuristics, developed for the specific problem, as well as with an Evolutionary Particle Swarm Optimization algorithm. The methods deliver a Pareto border, a set of several solutions, which the operator can choose between, depending on preferences. The optimization is built on component reliability importance indices, developed specifically for power systems. One vital aspect of the indices is that they work with several supply and load points simultaneously, addressing the multistate-reliability of power systems. For the computation of the indices both analytical and simulation based techniques are used. The indices constitute the connection between component reliability performance and system performance and so enable the maintenance optimization. The developed methods have been tested and improved in two case studies, based on real systems and data, proving the methods’ usefulness and showing that they are ready to be applied to power distribution systems. It is in addition noted that the methods could, with some modifications, be applied to other types of infrastructures. However, in order to perform the optimization, a reliability model of the studied power system is required, as well as estimates on effects of maintenance actions (changes in failure rate) and their related costs. Given this, a generally decreased level of total maintenance cost and a better system reliability performance can be given to the DSO and customers respectively. This is achieved by focusing the preventive maintenance to components with a high potential for improvement from system perspective. / QC 20100810

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No. of bitstreams: 1 quintella_lc_me_bauru.pdf: 4453734 bytes, checksum: b3c4369bb61bf3c735292c7280dfc961 (MD5) Previous issue date: 2016-07-04 / A função manutenção vem sendo considerada como fator estratégico para as empresas, pois através do alinhamento de suas políticas corporativas e integração de seus programas de gestão de ativos, de riscos e de ciclo de vida de suas unidades de negócios, as empresas vêm buscando a constante redução de custos e a melhoria de seus resultados operacionais. E, assim, obtendo maior competitividade. A Manutenção Centrada em Confiabilidade (RCM) é um método já bem disseminado por todo o mundo e que, ao longo dos anos, vem promovendo estes diferenciais estratégicos através de preceitos que possibilitam a elaboração de Programas de Manutenção Preventiva de custo-eficaz, através de um método para a definição de políticas de manutenção mais adequadas, com o foco na manutenção da função dos ativos em seu contexto operacional. Ao longo dos anos, o método RCM vem sendo aplicado em inúmeros estudos de casos em diferentes empresas de diversos seguimentos, onde podem ser observadas novas adaptações ou simplificações do método RCM clássico. Estas adaptações buscam uma maior adequação as particularidades destas empresas e/ou um retorno mais rápido de resultados. O setor nuclear de geração de energia foi um dos pioneiros na adoção e disseminação do RCM, e vem desenvolvendo processos simplificados de aplicação do RCM, como o “Streamlinned RCM” e o “método PMO” (Otimização do Programa de Manutenção, do inglês: Preventive Maintenance Optimization). Estes estudos mostram que o método PMO apresenta uma maior flexibilidade, o que permite a adoção de diferentes estratégias de aplicação que, por sua vez, têm trazido resultados expressivos para as empresas, através da otimização dos Programas de Manutenção já existentes. Com base na literatura, neste trabalho são abordadas questões referentes ao RCM e sua contextualização na área nuclear, estudos sobre os métodos simplificados do RCM e o desenvolvimento do método PMO. Por fim, é realizada uma aplicação prática do método PMO sobre os sistemas relacionados e subsistemas do Sistema de Remoção de Calor Residual (JN), mais especificamente, sobre as Bombas de injeção de segurança do Sistema de Injeção de Alta Pressão (JND) da Usina Nuclear Angra 2. Através dos resultados obtidos com esta aplicação, pretende-se otimizar o Programa de Manutenção da Planta (PMP) referente a estes equipamentos e, assim, validar o método PMO como ferramenta para a melhoria contínua do Programa de Gestão de Ativos e Ciclo de Vida da Usina Nuclear Angra 2. / Corporations tend to consider maintenance work a strategic element. It is through maintenance—especially the alignment between corporate policies and integration of their programs to manage assets, risks, and life cycles of business units—that corporations try to continuously improve their results, reduce their operational costs, and, therefore, increase their competitiveness. Reliability Centered Maintenance (RCM) is a popular method across the world; it has been promoting competitiveness through concepts that allow the design of cost-effective Preventive Maintenance Programs These programs are effective because they entail appropriate maintenance policies that are focused on the preservation of the assets functions in their operational context and on the formation of technical knowledge basis supported by hard data. Throughout the years, RCM has been applied in numerous case studies and in different companies engaged in a variety of market segments. Such diversity in the application of RCM allowed us to observe new adaptations and variations of the classic method. Such adaptations aim to better respond to specific operational contexts in and within production units, as well as achieve faster results. The nuclear power sector has pioneered regarding the adoption and dissemination of RCM; it has been developing simplified versions of RCM, such as the “Streamlined RCM” and the PMO (Preventive Maintenance Optimization). These studies demonstrate that the PMO presents enhanced flexibility, which allows the adoption of different strategies; such enhanced flexibility brings expressive results to corporations as pre-existing maintenance programs are optimized. Based on the currently available literature, this dissertation addresses numerous questions regarding RCM and its application to nuclear power segments. It also addresses studies about simplified versions of RCM and the development of the PMO method. The discussion is supplemented with a practical application of the PMO method regarding auxiliary systems and sub-systems of Removal of Residual Heat System (JN), especially those regarding the security injection pumps of the High Pressure Injection System (JND) at Angra II Nuclear Plant. Through the results obtained from this application, it is possible to optimize the Maintenance Program of these equipments, and therefore, validate the PMO method as a tool of continuous improvement of the Assets and Life Cycle Program of Angra II.

Maximum asset performance is one of the major goals for electric power system managers. To reach this goal minimal life cycle cost and maintenance optimization become crucial while meeting demands from customers and regulators. One of the fundamental objectives is therefore to relate maintenance and reliability in an efficiently and effectively way, which is the aim of several maintenance methods such as the Reliability Centered Maintenance method (RCM). Furthermore, this necessitates the determination of the optimal balance between preventive and corrective maintenance to obtain the lowest total cost. This thesis proposes methods for defining the importance of individual components in a network with respect to total interruption cost. This is a first step in obtaining an optimal maintenance solution. Since the methods consider several customer nodes simultaneously, they are especially suitable for network structures that serve many purposes/customers e.g. transmission and distribution networks with more than one load point. The major results are three component reliability importance indices, which are applied in two case studies. The first case study is based on a network in the Stockholm area. The second case study is performed for one overhead line system in the rural parts of Kristinehamn. The application studies demonstrate that the indices are possible to implement for existing electrical networks and that they can be used for maintenance prioritization. Consequently these indices constitute a first step in the overall objective of a maintenance optimization method. The computations of the indices are performed both with analytical and simulation based techniques. Furthermore, the indices can be used to calculate the component contribution to the total system interruption cost. The approach developed for the importance indices can be utilized in any multi-state network that can be measured with one performance indicator. / QC 20101130

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.

We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent. / text