No / This paper studies the selective maintenance problem for multi-state series systems working in variable loading conditions in the next mission. In the mission, a component's degradation depends on its current state and the load applied on it. A load-dependent degradation model is proposed for multi-state components operating in variable loading conditions. This model is inspired by the load-sharing model where many components share a common workload and the failure rate of a component depends on the state of other components. A Monte-Carlo simulation method is presented to simulate the multi-state component's degradation and to evaluate the system reliability. The final objective is to determine the best selective maintenance strategy to maximize the expected system reliability in the next mission within available resources. An illustrative example, reliability estimation results, and analysis of optimal selective maintenance scenarios for different levels of budget limitation are provided.

No / This paper studies the selective maintenance problem for multi-state systems with structural dependence. Each component can be in one of multiple working levels and several maintenance actions are possible to a component in a maintenance break. The components structurally form multiple hierarchical levels and dependence groups. A directed graph is used to represent the precedence relations of components in the system. A selective maintenance optimization model is developed to maximize the system reliability in the next mission under time and cost constraints. A backward search algorithm is used to determine the assembly sequence for a selective maintenance scenario. The maintenance model helps maintenance managers in determining the best combination of maintenance activities to maximize the probability of successfully completing the next mission. Examples showing the use of the proposed method are presented.

Yes / This paper presents a study on selective maintenance for multi-state series-parallel systems with economically dependent components. In the selective maintenance problem, the maintenance manager has to decide which components should receive maintenance activities within a finite break between missions. All the system reliabilities in the next operating mission, the available budget and the maintenance time for each component from its current state to a higher state are taken into account in the optimization models. In addition, the components in series-parallel systems are considered to be economically dependent. Time and cost savings will be achieved when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount resulting from the repair of multiple identical components. Different optimization models are derived to find the best maintenance strategy for multi-state series-parallel systems. A genetic algorithm is used to solve the optimization models. The decision makers may select different components to be repaired to different working states based on the maintenance objective, resource availabilities and how dependent the repair time and cost of each component are. © 2013 Elsevier Ltd. All rights reserved. / Natural Sciences and Engineering Research Council of Canada (NSERC) and Vietnam International Education Development (VIED)

Yes / In this paper, we will consider the selective maintenance problem for multistate series systems with stochastic dependent components. In multistate systems, the health state of a component may vary from perfect functioning to complete failure. The stochastic dependence (S-dependence) between components is discussed and categorized into two types in multistate context. First, the failure of a component can immediately cause complete failures of some other components in the system. Second, as components deteriorate, the reduced working performance rate of a multistate component affects the state as well as the degradation rate of its subsequent components in series structure. The system reliability is evaluated using an approach based on stochastic process. A cost-based selective maintenance model is developed for the multistate system with S-dependent components to maximize the total system profit, which includes the production gain and loss in the next mission as well as possible maintenance costs for the system. Analyses of systems with independent and dependent components are provided. It is observed that ignoring S-dependence in the system may lead to alternative maintenance decision making and an optimistic estimation of the system performance.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-08-05T14:27:22Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_.pdf: 1522093 bytes, checksum: bb2928d81ecdfb4d866a999cf07f0b76 (MD5) / Made available in DSpace on 2016-08-05T14:27:22Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_.pdf: 1522093 bytes, checksum: bb2928d81ecdfb4d866a999cf07f0b76 (MD5) Previous issue date: 2016-02-29 / CNPq / A escolha de uma estratégia adequada de manutenção para os sistemas produtivos de uma organização é um pré-requisito para a sua efetividade. Destaca-se que esta escolha é um processo decisório que utiliza modelos matemáticos para encontrar uma estratégia que otimize a relação custo-benefício das ações de manutenção. Tal estratégia deve levar em conta períodos para a execução das intervenções, bem como a existência de metas de produção. Embora seja comum a inclusão de paradas para manutenção entre períodos de operações dos sistemas nos planos de produção, geralmente aqueles modelos ignoram a finitude da duração de tais paradas; o mesmo acontece com relação à escassez dos recursos de manutenção. Entretanto, esses fatores restringem a quantidade de ações que podem ser executadas a cada parada e, portanto, não devem ser desprezados. Em virtude disso, é necessário identificar, dentre as ações de manutenção passíveis de serem executadas no sistema, aquelas que são críticas para seu(s) indicador(es) de performance. Isso é uma prerrogativa dos modelos de manutenção seletiva. Não obstante, a resolução desses modelos é tarefa árdua, já que problemas combinatórios necessitam de métodos heurísticos para serem solucionados. Neste trabalho, ambas as necessidades foram trabalhadas. O fim principal da investigação foi contribuir com os modelos de manutenção seletiva dedicados a sistemas orientados a múltiplas missões. Para isso, expandiu-se um modelo de manutenção seletiva voltado a sistemas orientados a multimissões e desenvolveu-se um algoritmo genético para resolver o problema. Estes foram postos à prova em uma aplicação numérica. Atestados a efetividade do método e a aplicabilidade do modelo, fez-se análises de sensibilidade e formulou-se modelos alternativos. Ainda se fez recomendações para a canalização de esforços em trabalhos futuros. No todo, a pesquisa configura-se como tendo natureza básica e aplicada, bem como caráter explicativo. Quanto à abordagem, é quantitativa e utiliza o método da modelagem e simulação. / The choice of a suitable maintenance strategy for productive systems of an organization is a requirement for its effectiveness. It is emphasized that this choice is a decision-making process that uses mathematical models to find a strategy that optimizes the cost-effectiveness of maintenance actions. This strategy must take into account periods for the implementation of assistance, as well as the existence of production goals. Although it is common to include maintenance downtime between periods of systems operations in production schedules, generally those models ignore the finite length of these stops; the same applies regarding to the shortage of maintenance resources. However, these factors constrain the number of actions that can be performed at each break and, therefore, should not be neglected. As a result, it is necessary to identify, among the maintenance actions that can be performed on the system, those that are critical to the performance indicator(s). This is a prerogative of selective maintenance models. Nevertheless, the resolution of these models is a difficult task, as combinatorial problems require heuristic methods to be solved. In this work, both requirements were addressed. The main purpose of the research was to contribute to the selective maintenance models dedicated to multiple mission’s oriented systems. For this it has been expanded a multiple mission oriented maintenance model and was developed a genetic algorithm for solving the problem. These were tested in a numerical application. Attested the effectiveness of the method and the applicability of the model, a sensitivity analysis were performed and alternative models were developed. Still it has made recommendations for concentrating efforts in future work. Overall, the research is characterized as having basic and applied nature, as well as explanatory character. As for the approach, it is quantitative and uses the modeling and simulation method.

Cette thèse porte sur le développement, l'évaluation et l'optimisation de nouvelles stratégies de maintenance pour des systèmes multi-composants. Cette démarche est justifiée par le fait que contrairement aux systèmes monolithiques qui ont été largement traités dans la littérature sur la maintenance, les systèmes multi-composants avec dépendances économique et stochastique sont encore peu étudiés à cause essentiellement de la difficulté de modélisation de ces types de dépendance. Dans ce cadre d'étude de la maintenance des systèmes multi-composants, cette thèse contient trois volets indépendants. Le premier volet porte sur un type particulier d'équipements dont l'état ne peut être connu que suite à une inspection. De tels équipements constitués de deux composants en série sont considérés. Une nouvelle stratégie quasi-optimale de maintenance conditionnelle basée sur des inspections séquentielles des deux composants est proposée. Dans le deuxième volet on considère des systèmes multi-composants complexes dans le sens où leurs composants peuvent être sujets à des défaillances aléatoires locales et/ou propagées avec ou sans effet d'isolation. Une stratégie de maintenance sélective est proposée pour ce type de systèmes. Dans le troisième et dernier volet la notion de systèmes multi-composants est étendue aux systèmes de production multi-lignes. Dans cette perspective, cette partie porte sur le développement d'une politique intégrée de production-maintenance pour un système de production à deux lignes fonctionnant en parallèle en présence d'une dépendance de type stochastique entre les deux lignes / This thesis focuses on the development, the evaluation and the optimization of new maintenance policies for multi-component systems. This approach is justified by the fact that contrarily to single component systems which have been extensively treated in the literature on maintenance strategies, multi-component systems with economic and stochastic dependence have been much less studied due to the difficulty in modeling such kind of dependence. In this context of studying multi-component systems maintenance, this thesis is made of three independent parts. The first part focuses on a particular type of equipment whose state can only be known following inspection. Such equipment made of two components in series is considered. A new nearly optimal condition based maintenance policy based on sequential inspections of both components is proposed. In the second part, we consider complex multi-component systems in which the components are subject to random local and/or propagated failures, with or without isolation effect. A selective maintenance strategy is applied to such systems. In the third and last part, the concept of multi-component systems is extended to manufacturing systems with multiple machines. In this context, this part focuses on the development of an integrated production-maintenance policy for a production system consisting of two machines in parallel in presence of a form of stochastic dependency between them