• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace Home
  • 1- Fakülteler | Faculties
  • Mühendislik Fakültesi / Faculty of Engineering
  • Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
  • MF - Makale Koleksiyonu | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
  • View Item
  •   DSpace Home
  • 1- Fakülteler | Faculties
  • Mühendislik Fakültesi / Faculty of Engineering
  • Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
  • MF - Makale Koleksiyonu | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A DBN based reactive maintenance model for a complex system in thermal power plants

Thumbnail

View/Open

Publisher's Version (4.093Mb)

Date

2019-10

Author

Özgür Ünlüakın, Demet
Türkali, Busenur
Karacaörenli, Ayse
Aksezer, Sezgin Çağlar

Metadata

Show full item record

Citation

Özgür-Ünlüakın, D., Türkali, B., Karacaörenli, A., & Çağlar Aksezer, S. (2019). A DBN based reactive maintenance model for a complex system in thermal power plants. Reliability Engineering and System Safety, 190, 106505. doi:10.1016/j.ress.2019.106505

Abstract

Thermal power plants consist of several complex systems having many interacting hidden components. Any unexpected failure may lead to prolonged downtime and serious lost profits. Therefore, implementing an effective maintenance policy is crucial for this sector. Although preventive maintenance has become a more popular strategy, it does not completely prevent the need for corrective maintenance. Our aim in this study is to tackle the corrective maintenance implementation problem of a multi-component partially observable dynamic system based on a regenerative air heater in a thermal power plant. We propose eight methods having different efficiency measures with respect to time, effect and probability criteria to minimize the total number of maintenance activities in a given planning horizon. Performances of these methods are evaluated under corrective maintenance strategy using dynamic Bayesian networks. The results show that fault effect methods with best working state probability measure perform better than the others considering both the total amount of maintenance activities and also the solution time. We also point out how the methods can be implemented in real-life and how the results can be used for requirements planning. Furthermore, the proposed methods can be used for the corrective maintenance of all systems having hidden interacting components.

Source

Reliability Engineering and System Safety

Volume

190

URI

https://dx.doi.org/10.1016/j.ress.2019.106505
https://hdl.handle.net/11729/1692

Collections

  • Makale Koleksiyonu [807]
  • Makale Koleksiyonu [708]
  • MF - Makale Koleksiyonu | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering [25]



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Policy | Guide | Contact |

DSpace@Işık

by OpenAIRE
Advanced Search

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeIşık AuthorCitationThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeIşık AuthorCitation

My Account

LoginRegister

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Policy || Guide || Library || Işık University || OAI-PMH ||

Işık University Library, Şile, İstanbul, Turkey
If you find any errors in content please report us

Creative Commons License
Işık University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace@Işık:


DSpace 6.2

tarafından İdeal DSpace hizmetleri çerçevesinde özelleştirilerek kurulmuştur.