Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/220571 |
Resumo: | Currently, a number of researchers have been working to understand the health monitoring and damage detection problems. Structural health monitoring (SHM) and damage detection techniques are instrumental for the engineering community both for safety and cost effectiveness reasons. The project herein demonstrates that maintenance can be planned before a fault occurs, minimizing thus serious damages probability. A customized conditional maintenance design has been developed by means of SHM and damage techniques. Such a system provides fixed bands as well as trend graphics which estimate a possible fault alarm, emergency time and detection damage as well. The artificial neural network theory has been the tool used for its fast detecting and determining damages on an operating machine before critical conditions, which leads to an optimized maintenance and production management. |
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Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectivenessCondition monitoringMaintenance managementNeural networkVibrationCurrently, a number of researchers have been working to understand the health monitoring and damage detection problems. Structural health monitoring (SHM) and damage detection techniques are instrumental for the engineering community both for safety and cost effectiveness reasons. The project herein demonstrates that maintenance can be planned before a fault occurs, minimizing thus serious damages probability. A customized conditional maintenance design has been developed by means of SHM and damage techniques. Such a system provides fixed bands as well as trend graphics which estimate a possible fault alarm, emergency time and detection damage as well. The artificial neural network theory has been the tool used for its fast detecting and determining damages on an operating machine before critical conditions, which leads to an optimized maintenance and production management.Laboratory of Vibration and Instrumentation (LVI) Universidade Estadual Paulista (UNESP) Faculdade de Engenharia de Ilha Solteira, Av. Brazil 56Laboratory of Vibration and Instrumentation (LVI) Universidade Estadual Paulista (UNESP) Faculdade de Engenharia de Ilha Solteira, Av. Brazil 56Universidade Estadual Paulista (UNESP)Arato, Adyles [UNESP]Almeida, Fabrício [UNESP]2022-04-28T19:03:00Z2022-04-28T19:03:00Z2008-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject7th European Conference on Structural Dynamics, EURODYN 2008.http://hdl.handle.net/11449/2205712-s2.0-84959861165Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng7th European Conference on Structural Dynamics, EURODYN 2008info:eu-repo/semantics/openAccess2022-04-28T19:03:00Zoai:repositorio.unesp.br:11449/220571Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:19:55.118366Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
title |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
spellingShingle |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness Arato, Adyles [UNESP] Condition monitoring Maintenance management Neural network Vibration |
title_short |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
title_full |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
title_fullStr |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
title_full_unstemmed |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
title_sort |
Conditional maintenance using artificial neural network and vibration techniques to improve production cost-effectiveness |
author |
Arato, Adyles [UNESP] |
author_facet |
Arato, Adyles [UNESP] Almeida, Fabrício [UNESP] |
author_role |
author |
author2 |
Almeida, Fabrício [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Arato, Adyles [UNESP] Almeida, Fabrício [UNESP] |
dc.subject.por.fl_str_mv |
Condition monitoring Maintenance management Neural network Vibration |
topic |
Condition monitoring Maintenance management Neural network Vibration |
description |
Currently, a number of researchers have been working to understand the health monitoring and damage detection problems. Structural health monitoring (SHM) and damage detection techniques are instrumental for the engineering community both for safety and cost effectiveness reasons. The project herein demonstrates that maintenance can be planned before a fault occurs, minimizing thus serious damages probability. A customized conditional maintenance design has been developed by means of SHM and damage techniques. Such a system provides fixed bands as well as trend graphics which estimate a possible fault alarm, emergency time and detection damage as well. The artificial neural network theory has been the tool used for its fast detecting and determining damages on an operating machine before critical conditions, which leads to an optimized maintenance and production management. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 2022-04-28T19:03:00Z 2022-04-28T19:03:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
7th European Conference on Structural Dynamics, EURODYN 2008. http://hdl.handle.net/11449/220571 2-s2.0-84959861165 |
identifier_str_mv |
7th European Conference on Structural Dynamics, EURODYN 2008. 2-s2.0-84959861165 |
url |
http://hdl.handle.net/11449/220571 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
7th European Conference on Structural Dynamics, EURODYN 2008 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128634505920512 |