No / Many industrial systems such as aircrafts, ships, manufacturing systems, etc. are required to perform several missions with finite breaks between missions. Maintenance is only available within the breaks. Due to the limitation of resources, all components in the system may not be maintained as desired. The selective maintenance problem helps the decision makers figure out what critical components to select and how to perform maintenance on these components. This paper studies the selective maintenance for multi-state series-parallel systems with the benefit of repairing multiple components simultaneously. Both time and cost savings can be acquired 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 from the repair of multiple identical components. A non-linear optimization model is developed to find the most reliable system subjected to time and cost constraints. Genetic algorithm is used to solve the optimization model. An illustrative example will be provided. / Natural Sciences and Engineering Research Council of Canada (NSERC) and Vietnam International Education Development (VIED)

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)

Technische Systeme nutzen sich ab. Dadurch bedingt kommt es zu Ausfällen. Um die Funktionstüchtigkeit von abgenutzten technischen Systemen wiederherzustellen, werden Instandsetzungsmaßnahmen durchgeführt. Da die Folgen eines unerwartet eintretenden Ausfalls drastisch sein können, ist es sinnvoll, das Abnutzungsverhalten eines technischen Systems vorherzusagen und so den Zeitpunkt von Instandsetzungsmaßnahmen zielgerichtet zu planen. Die Erstellung von Abnutzungsprognosen wird dadurch erschwert, dass sich technische Systeme oft variabel, in Abhängigkeit von auf sie einwirkenden Beanspruchungen, abnutzen. Außerdem wird diese Abnutzungsvariabilität von betriebsbedingten Einflüssen überlagert, was deren Modellierung erschwert. Im Rahmen dieser Arbeit wurden deshalb Lösungsansätze entwickelt, die es ermöglichen, die Abnutzungsvariabilität eines technischen Systems in Abnutzungsprognosen zu integrieren und dabei betriebsbedingte Einflüsse zu berücksichtigen. Somit können Instandsetzungsmaßnahmen präziser geplant, Ressourcen geschont sowie Kosten reduziert werden. / Technical systems are prone to deterioration. This leads to negative consequences like break-downs. Maintenance actions are executed in order to transfer technical systems back into healthy states. If break downs occur suddenly, the consequences can be dramatic. Therefore, it is reasonable to schedule maintenance actions based on health-state predictions. Thereby, health state predictions are impeded by the fact that technical systems often deteriorate variably, depending on certain stress factors. Furthermore, the effects of variable deterioration behavior can be hidden by system specific behavior. Thus, approaches are shown which integrate variable deterioration behavior into healthstate predictions while influences caused by the system specific behavior are considered. Consequently, maintenance actions can be scheduled more efficiently which spares resources and reduces costs.