Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa operacional (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382009000300006 |
Resumo: | The use of stochastic point processes to model the reliability of repairable systems has been a regular approach to establish survival measures in failure versus repair scenarios. However, the traditional processes do not consider the actual state in which an item returns to operational condition. The traditional renewal process considers an "as-good-as-new" philosophy, while a non-homogeneous Poisson process is based on the minimal repair concept. In this work, an approach based on the concept of Generalized Renewal Process (GRP) is presented, which is a generalization of the renewal process and the non-homogeneous Poisson process. A stochastic modeling is presented for systems availability analysis, including testing and/or preventive maintenances scheduling. To validate the proposed approach, it was performed a case study of a hypothetical auxiliary feed-water system of a nuclear power plant, using genetic algorithm as optimization tool. |
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Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal processavailabilitygeneralized renewal processgenetic algorithmsThe use of stochastic point processes to model the reliability of repairable systems has been a regular approach to establish survival measures in failure versus repair scenarios. However, the traditional processes do not consider the actual state in which an item returns to operational condition. The traditional renewal process considers an "as-good-as-new" philosophy, while a non-homogeneous Poisson process is based on the minimal repair concept. In this work, an approach based on the concept of Generalized Renewal Process (GRP) is presented, which is a generalization of the renewal process and the non-homogeneous Poisson process. A stochastic modeling is presented for systems availability analysis, including testing and/or preventive maintenances scheduling. To validate the proposed approach, it was performed a case study of a hypothetical auxiliary feed-water system of a nuclear power plant, using genetic algorithm as optimization tool.Sociedade Brasileira de Pesquisa Operacional2009-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382009000300006Pesquisa Operacional v.29 n.3 2009reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/S0101-74382009000300006info:eu-repo/semantics/openAccessDamaso,Vinícius CorreaGarcia,Pauli Adriano de Almadaeng2010-02-03T00:00:00Zoai:scielo:S0101-74382009000300006Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2010-02-03T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.fl_str_mv |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
title |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
spellingShingle |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process Damaso,Vinícius Correa availability generalized renewal process genetic algorithms |
title_short |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
title_full |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
title_fullStr |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
title_full_unstemmed |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
title_sort |
Testing and preventive maintenance scheduling optimization for aging systems modeled by generalized renewal process |
author |
Damaso,Vinícius Correa |
author_facet |
Damaso,Vinícius Correa Garcia,Pauli Adriano de Almada |
author_role |
author |
author2 |
Garcia,Pauli Adriano de Almada |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Damaso,Vinícius Correa Garcia,Pauli Adriano de Almada |
dc.subject.por.fl_str_mv |
availability generalized renewal process genetic algorithms |
topic |
availability generalized renewal process genetic algorithms |
description |
The use of stochastic point processes to model the reliability of repairable systems has been a regular approach to establish survival measures in failure versus repair scenarios. However, the traditional processes do not consider the actual state in which an item returns to operational condition. The traditional renewal process considers an "as-good-as-new" philosophy, while a non-homogeneous Poisson process is based on the minimal repair concept. In this work, an approach based on the concept of Generalized Renewal Process (GRP) is presented, which is a generalization of the renewal process and the non-homogeneous Poisson process. A stochastic modeling is presented for systems availability analysis, including testing and/or preventive maintenances scheduling. To validate the proposed approach, it was performed a case study of a hypothetical auxiliary feed-water system of a nuclear power plant, using genetic algorithm as optimization tool. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382009000300006 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382009000300006 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0101-74382009000300006 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
Pesquisa Operacional v.29 n.3 2009 reponame:Pesquisa operacional (Online) instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO |
instname_str |
Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
instacron_str |
SOBRAPO |
institution |
SOBRAPO |
reponame_str |
Pesquisa operacional (Online) |
collection |
Pesquisa operacional (Online) |
repository.name.fl_str_mv |
Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
repository.mail.fl_str_mv |
||sobrapo@sobrapo.org.br |
_version_ |
1750318016996835328 |