A DBN based prognosis model for a complex dynamic system: a case study in a thermal power plant

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dc.authorid0000-0002-7414-2330
dc.authorid0000-0002-3835-7684
dc.authorid0000-0002-1150-7064
dc.contributor.authorÖzgür Ünlüakın, Demeten_US
dc.contributor.authorKıvanç, İpeken_US
dc.contributor.authorTürkali, Busenuren_US
dc.contributor.authorAksezer, Sezgin Çağlaren_US
dc.date.accessioned2026-01-06T08:07:58Z
dc.date.available2026-01-06T08:07:58Z
dc.date.issued2018-08-15
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Industrial Engineeringen_US
dc.description.abstractWith 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.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÖzgür Ünlüakın, D., Kıvanç, İ., Türkali, B. & Aksezer, S. Ç. (2018). A DBN based prognosis model for a complex dynamic system: a case study in a thermal power plant. Paper presented at the Proceedings of the International Symposium for Production Research 2018, 75-84. doi:https://dx.doi.org/10.1007/978-3-319-92267-6_6en_US
dc.identifier.endpage84
dc.identifier.isbn9783319922669
dc.identifier.isbn9783319922676
dc.identifier.startpage75
dc.identifier.urihttps://hdl.handle.net/11729/6876
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-92267-6_6
dc.institutionauthorÖzgür Ünlüakın, Demeten_US
dc.institutionauthorTürkali, Busenuren_US
dc.institutionauthorAksezer, Sezgin Çağlaren_US
dc.institutionauthorid0000-0002-7414-2330
dc.institutionauthorid0000-0002-3835-7684
dc.institutionauthorid0000-0002-1150-7064
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublished
dc.publisherSpringer Nature Switzerland AGen_US
dc.relation.ispartofProceedings of the International Symposium for Production Research 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPower plantsen_US
dc.subjectMulti-component systemsen_US
dc.subjectPrognosis reliabilityen_US
dc.subjectMaintenanceen_US
dc.subjectDBNsen_US
dc.titleA DBN based prognosis model for a complex dynamic system: a case study in a thermal power planten_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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