A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems
Autor(a) principal: | |
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Data de Publicação: | 2008 |
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=S0101-74382008000200011 |
Resumo: | In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems. |
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A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systemssemi-Markov processesBayesian belief networksLaplace transformsavailability measurefault tolerant systemsIn this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.Sociedade Brasileira de Pesquisa Operacional2008-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382008000200011Pesquisa Operacional v.28 n.2 2008reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/S0101-74382008000200011info:eu-repo/semantics/openAccessMoura,Márcio das ChagasDroguett,Enrique Lópezeng2008-10-20T00:00:00Zoai:scielo:S0101-74382008000200011Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2008-10-20T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.fl_str_mv |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
title |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
spellingShingle |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems Moura,Márcio das Chagas semi-Markov processes Bayesian belief networks Laplace transforms availability measure fault tolerant systems |
title_short |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
title_full |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
title_fullStr |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
title_full_unstemmed |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
title_sort |
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems |
author |
Moura,Márcio das Chagas |
author_facet |
Moura,Márcio das Chagas Droguett,Enrique López |
author_role |
author |
author2 |
Droguett,Enrique López |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Moura,Márcio das Chagas Droguett,Enrique López |
dc.subject.por.fl_str_mv |
semi-Markov processes Bayesian belief networks Laplace transforms availability measure fault tolerant systems |
topic |
semi-Markov processes Bayesian belief networks Laplace transforms availability measure fault tolerant systems |
description |
In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-08-01 |
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=S0101-74382008000200011 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382008000200011 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0101-74382008000200011 |
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.28 n.2 2008 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_ |
1750318016941260800 |