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dc.contributor.advisorÖzgür Ünlüakın, Demeten_US
dc.contributor.authorTürkali, Busenuren_US
dc.contributor.otherIşık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans Programıen_US
dc.date.accessioned2020-11-26T14:06:37Z
dc.date.available2020-11-26T14:06:37Z
dc.date.issued2020-08-12
dc.identifier.citationTürkali, B. (2020). Evaluation of alternative maintenance strategies on a complex system in thermal power systems. İstanbul: Işık Üniversitesi Fen Bilimleri Enstitüsü.en_US
dc.identifier.urihttps://hdl.handle.net/11729/2970
dc.descriptionText in English ; Abstract: English and Turkishen_US
dc.descriptionIncludes bibliographical references (leaves 100-108)en_US
dc.descriptionxv, 108 leavesen_US
dc.description.abstractIn recent years, due to the continuous development of the industry and the rapid increase in the system complexity, maintenance policies have become more important. Unplanned downtimes due to unexpected failures may lead to huge problems in almost all industry branch. However, carrying out maintenance more than the required to prevent unexpected failures increases maintenance cost signicantly. Thus, balancing the number of reactive and proactive maintenance is very important. The aim of this thesis is to develop maintenance methods under the reactive, condition-based and proactive maintenance strategies using dynamic Bayesian networks (DBNs) in thermal power plants. DBNs which are are probabilistic graphical models, are selected to model the system because they are very effective to formulate the stochastic and structural dependencies between the components. In this study, we evaluate alternative maintenance strategies on a complex systembased on two factors: total number of maintenance and total maintenance cost in a given planning horizon. The proposed maintenance methods are simulated on a multi-component thermal power plant system which has a very complex structure with hidden components among which there are stochastic and structural dependencies. Scenarios are designed considering the maintenance dependability of parallel systems during proactive activities and different reactive cost structures. As a result, performances of all proposed maintenance strategies and methods are compared and analysed under each scenario and the most promising ones are highlighted.en_US
dc.description.abstractSon yıllarda, endüstrinin sürekli gelişimi ve sistemlerin karmaşıklığının artması ile bakım politikaları daha önemli hale gelmiştir. Beklenmedik arızalar nedeniyle ortaya çıkan planlanmayan arıza süreleri, hemen hemen tüm endüstri kollarında büyük sorunlara yol açabilir. Ancak, beklenmedik arızaları önlemek için gereğinden fazla bakım yapılması da bakım maliyetlerini önemli ölçüde artırır. Bu nedenle, reaktif ve proaktif bakım sayısının dengelenmesi çok önemlidir. Bu tezin amacı, termik santrallerde olasılıklı grafik modeller olan dinamik Bayes ağlarını (DBN'ler) kullanarak reaktif, koşul bazlı ve proaktif bakım stratejileri kapsamında bakım yöntemleri geliştirmektir. Sistemi modellemek için bileşenler arasındaki yapısal ve stokastik bağımlılıkları formüle etmek için çok etkili olan DBN'ler seçilmiştir. Bu çalışmada, karmaşık bir sistemde alternatif bakım stratejileri iki faktöre dayanılarak değerlendirilmiştir: belirli bir planlama ufkunda toplam bakım sayısı ve toplam bakım maliyeti. Önerilen bakım yöntemleri, aralarında rassal ve yapısal bağımlılıklar olan gizli bileşenlerin bulunduğu çok karmaşık yapıya sahip çok bileşenli bir termik santral sisteminde simüle edilmiştir. Paralel sistemlerin bakım bağımlılıkları ve farklı reaktif bakım maliyetleri dikkate alınarak senaryolar oluşturulmuştur. Sonuç olarak, önerilen tüm bakım stratejilerinin ve yöntemlerinin performansları her senaryo altında karşılaştırılmış ve analiz edilmiş, en iyi bulunan yöntemler açıklanmıştır.en_US
dc.description.tableofcontentsIntroductionen_US
dc.description.tableofcontentsClassification of Maintenance Philosophiesen_US
dc.description.tableofcontentsReactive Maintenance Strategiesen_US
dc.description.tableofcontentsProactive Maintenance Strategiesen_US
dc.description.tableofcontentsPreventive Maintenance Strategiesen_US
dc.description.tableofcontentsPredictive Maintenance Strategiesen_US
dc.description.tableofcontentsDependencies in Multi-Component Systemsen_US
dc.description.tableofcontentsStructural Dependencyen_US
dc.description.tableofcontentsEconomic Dependencyen_US
dc.description.tableofcontentsStochastic Dependencyen_US
dc.description.tableofcontentsResource Dependencyen_US
dc.description.tableofcontentsMethods for Modeling the Dependenciesen_US
dc.description.tableofcontentsMotivation of the Thesisen_US
dc.description.tableofcontentsOrganization of the Thesisen_US
dc.description.tableofcontentsLiterature Surveyen_US
dc.description.tableofcontentsEvolution of Dynamic Bayesian Networksen_US
dc.description.tableofcontentsApplications of DBNs in Maintenance and Related Fieldsen_US
dc.description.tableofcontentsMaintenance in Complex Systemsen_US
dc.description.tableofcontentsMaintenance in Thermal Power Plantsen_US
dc.description.tableofcontentsMethodology and Solution Approachen_US
dc.description.tableofcontentsProbabilistic Graphical Modelsen_US
dc.description.tableofcontentsBayesian Networks and Their Usage in Dependent Systemsen_US
dc.description.tableofcontentsDynamic Bayesian Networks in Dependent Systemsen_US
dc.description.tableofcontentsReactive Maintenance Strategyen_US
dc.description.tableofcontentsGeneral Flow of Reactive Maintenanceen_US
dc.description.tableofcontentsFailure Effect Myopic Methods (FEMfp, FEMwp)en_US
dc.description.tableofcontentsFailure Effect Look-Ahead Methods (FELfp, FELwp)en_US
dc.description.tableofcontentsReplacement Effect Myopic Methods (REMfp, REMwp)en_US
dc.description.tableofcontentsReplacement Effect Look-Ahead Methods (RELfp, RELwp)en_US
dc.description.tableofcontentsBrief Summary of the Proposed Methodsen_US
dc.description.tableofcontentsNormalization Procedureen_US
dc.description.tableofcontentsProactive Maintenance Strategyen_US
dc.description.tableofcontentsTabu Procedureen_US
dc.description.tableofcontentsConstant Interval Proactive Maintenance (CIPM)en_US
dc.description.tableofcontentsDynamic Interval Proactive Maintenance (DIPM)en_US
dc.description.tableofcontentsThreshold Based Proactive Maintenance (ThPM)en_US
dc.description.tableofcontentsGeneric Algorithm of the Proactive Maintenance Strategiesen_US
dc.description.tableofcontentsA Case Study: The Regenerative Air Heater System in Thermal Power Plantsen_US
dc.description.tableofcontentsA Thermal Power Planten_US
dc.description.tableofcontentsAir-Gas System in a Thermal Power Planten_US
dc.description.tableofcontentsThe Regenerative Air Heater Systemen_US
dc.description.tableofcontentsDBN Modeling of the RAH Systemen_US
dc.description.tableofcontentsVariables and Their Statesen_US
dc.description.tableofcontentsSystem Relationshipsen_US
dc.description.tableofcontentsProbability Structureen_US
dc.description.tableofcontentsCost Structureen_US
dc.description.tableofcontentsComputational Results and Evaluationsen_US
dc.description.tableofcontentsResults of Reactive Maintenance Modelingen_US
dc.description.tableofcontentsReplication Results Regarding to Total Maintenance Numberen_US
dc.description.tableofcontentsComparison Results of the Proposed Methodsen_US
dc.description.tableofcontentsMaintenance Supply Planning of Componentsen_US
dc.description.tableofcontentsReplication Results Regarding to Total Maintenance Costen_US
dc.description.tableofcontentsJustification of the Normalization Procedureen_US
dc.description.tableofcontentsReplication Results with Normalization Procedureen_US
dc.description.tableofcontentsSensitivity Analysis of the Proposed Methods according to Hourly Downtime Costen_US
dc.description.tableofcontentsTrade-off Analysis: Maintenance Cost vs Maintenance Numberen_US
dc.description.tableofcontentsAnalysis of Number - Based and Cost – Based Methods at Component Levelen_US
dc.description.tableofcontentsResults of Proactive Maintenance Modelingen_US
dc.description.tableofcontentsScenarios Based on Independent Parallel Engine Groupsen_US
dc.description.tableofcontentsScenario DCR25en_US
dc.description.tableofcontentsScenario DCR50en_US
dc.description.tableofcontentsScenario DCR50 - 2*ADen_US
dc.description.tableofcontentsScenarios Based on Dependent Parallel Engine Groupsen_US
dc.description.tableofcontentsScenario depDCR25en_US
dc.description.tableofcontentsScenario depDCR50en_US
dc.description.tableofcontentsScenario depDCR50-2*ADen_US
dc.description.tableofcontentsComparison of the Strategies using the Best Parametersen_US
dc.description.tableofcontentsJustification of Using Tabu Procedureen_US
dc.description.tableofcontentsNumber and Cost Distribution of the Componentsen_US
dc.description.tableofcontentsConclusionen_US
dc.description.tableofcontentsInitial probabilities of the RAH DBN modelen_US
dc.description.tableofcontentsTransition probabilities of the RAH DBN modelen_US
dc.description.tableofcontentsConditional probabilities of the RAH DBN modelen_US
dc.language.isoengen_US
dc.publisherIşık Üniversitesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectDBNen_US
dc.subjectReactive maintenanceen_US
dc.subjectProactive maintenanceen_US
dc.subjectComplex systemsen_US
dc.subjectDinamik Bayesçi ağlaren_US
dc.subjectDüzeltici bakımen_US
dc.subjectProaktif bakımen_US
dc.subjectKompleks sistemleren_US
dc.subject.lccTS192 .T87 2020
dc.subject.lcshIndustrial equipment -- Maintenance and repairen_US
dc.subject.lcshReactive maintenanceen_US
dc.subject.lcshProactive maintenanceen_US
dc.subject.lcshComplex systemsen_US
dc.subject.lcshDBNen_US
dc.subject.lcshPower-plants -- Maintenance and repairen_US
dc.titleEvaluation of alternative maintenance strategies on a complex system in thermal power systemsen_US
dc.title.alternativeTermik santrallerde kullanılan karmaşık bir sistem üzerinde alternatif bakım stratejilerinin değerlendirilmesien_US
dc.typemasterThesisen_US
dc.contributor.departmentIşık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans Programıen_US
dc.contributor.authorID0000-0002-3835-7684
dc.relation.publicationcategoryTezen_US
dc.contributor.institutionauthorTürkali, Busenuren_US


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