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Yayın Analysis of different maintenance policies on a multi-component system using dynamic bayesian networks(Işık Üniversitesi, 2019-01-15) Karacaörenli, Ayşe; Özgür Ünlüakın, Demet; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans ProgramıRecently, system components and interactions between them have become more complex and this situation has made it di?cult to provide maintenance decisions. Herewith, determining e?ective decisions has played an important role. In multicomponent systems, many methodologies and strategies can be applied when a component or a system has already broken down or when it is desired to identify and avoid pro-actively defects that could lead to future failure. In dynamic systems, it is important for proactive maintenance to increase system reliability by performing early diagnosis-based maintenance activities without waiting for a problem. In this study, we focus on proactive maintenance of a complex multi-component dynamic system. Components are hidden although there exists partial observability to the decision maker. Components deteriorate in time. It is possible to replace or repair components with a given cost. We want to ?nd a policy that minimizes the total maintenance cost in a prede?ned time horizon. We propose several maintenance policies and compare the performance of these by simulating them via Dynamic Bayesian Networks on an empirical model. Furthermore, a dynamic Bayesian network is constructed for the maintenance of an endo generator system to show how the proposed methods can be implemented in real life.Yayın A DBN based prognosis model for a complex dynamic system: a case study in a thermal power plant(Springer Nature Switzerland AG, 2018-08-15) Özgür Ünlüakın, Demet; Kıvanç, İpek; Türkali, Busenur; Aksezer, Sezgin ÇağlarWith the development of industry, complexity of systems and equipment has increased extensively. This results in the introduction of many interdependencies (stochastic, structural and economic) among the components of systems. Neglecting these interdependencies, when planning maintenance actions, leads to undesirable outcomes such as prolonged downtime and higher costs. That is why a multi-component system approach needs to be taken into account in maintenance planning models. However, maintenance planning is a difficult task in multi-component systems because of their complexities. Energy production systems are notable examples of such complex structures consisting of many interacting components. Maintenance planning is extremely crucial for this sector since any unexpected malfunction leads to very serious costs. Therefore, the aim of this study is to formulate the maintenance problem of a multi-component dynamic system in thermal power plants focusing on system reliability prognosis. Bayesian networks (BN) are probabilistic graphical models that have been extensively used to represent and model the causal relations. A dynamic Bayesian network (DBN) is an extended BN which has a temporal dimension. We propose to use DBNs to prognose the reliabilities of components and processes of a dynamic system in a thermal power plant and show that this representation is efficient to model the interdependencies and degradations in such a system.
