Fault detection using neuro-fuzzy networks
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/10789 |
Resumo: | Generally three methodologies to develop and test fault detection (FD) algorithms can be distingguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench that consists of process under supervision (two interconnected stations), supervision unit, fault diagnosis unit and fault simulation unit. All elements of the bench are connected to a PROFIBUS network that acts as the communication system exchaging information between automation system and distributed field devices. A realistic and fexible environment for developing and testing FD systems has been constructed using elements commonly used in industry. During the current studies actuator faults, sensor faults and leakages have been considered as incipiente and abrupt faults. The proposed FD algorithm bases on neuro-fuzzy models that are responsible for residual generation. |
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Fault detection using neuro-fuzzy networksFault detectionNeuro-fuzzy networkModellingClustering algorithmsGenerally three methodologies to develop and test fault detection (FD) algorithms can be distingguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench that consists of process under supervision (two interconnected stations), supervision unit, fault diagnosis unit and fault simulation unit. All elements of the bench are connected to a PROFIBUS network that acts as the communication system exchaging information between automation system and distributed field devices. A realistic and fexible environment for developing and testing FD systems has been constructed using elements commonly used in industry. During the current studies actuator faults, sensor faults and leakages have been considered as incipiente and abrupt faults. The proposed FD algorithm bases on neuro-fuzzy models that are responsible for residual generation.Oficyna Wydawnicza Politechniki WrocławskiejRCIPLKowal, MarekKorbicz, JózefMendes, Mário J. G. C.Calado, João Manuel Ferreira2019-12-03T14:27:20Z20022002-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/10789engKOWAL, Marek; [et al] – Fault detection using neuro-fuzzy networks. Systems Science. ISSN 0137-1223. Vol. 28, N.º 1 (2002), pp. 45-570137-1223metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T10:01:13Zoai:repositorio.ipl.pt:10400.21/10789Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:19:08.294758Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Fault detection using neuro-fuzzy networks |
title |
Fault detection using neuro-fuzzy networks |
spellingShingle |
Fault detection using neuro-fuzzy networks Kowal, Marek Fault detection Neuro-fuzzy network Modelling Clustering algorithms |
title_short |
Fault detection using neuro-fuzzy networks |
title_full |
Fault detection using neuro-fuzzy networks |
title_fullStr |
Fault detection using neuro-fuzzy networks |
title_full_unstemmed |
Fault detection using neuro-fuzzy networks |
title_sort |
Fault detection using neuro-fuzzy networks |
author |
Kowal, Marek |
author_facet |
Kowal, Marek Korbicz, Józef Mendes, Mário J. G. C. Calado, João Manuel Ferreira |
author_role |
author |
author2 |
Korbicz, Józef Mendes, Mário J. G. C. Calado, João Manuel Ferreira |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Kowal, Marek Korbicz, Józef Mendes, Mário J. G. C. Calado, João Manuel Ferreira |
dc.subject.por.fl_str_mv |
Fault detection Neuro-fuzzy network Modelling Clustering algorithms |
topic |
Fault detection Neuro-fuzzy network Modelling Clustering algorithms |
description |
Generally three methodologies to develop and test fault detection (FD) algorithms can be distingguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench that consists of process under supervision (two interconnected stations), supervision unit, fault diagnosis unit and fault simulation unit. All elements of the bench are connected to a PROFIBUS network that acts as the communication system exchaging information between automation system and distributed field devices. A realistic and fexible environment for developing and testing FD systems has been constructed using elements commonly used in industry. During the current studies actuator faults, sensor faults and leakages have been considered as incipiente and abrupt faults. The proposed FD algorithm bases on neuro-fuzzy models that are responsible for residual generation. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002 2002-01-01T00:00:00Z 2019-12-03T14:27:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/10789 |
url |
http://hdl.handle.net/10400.21/10789 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
KOWAL, Marek; [et al] – Fault detection using neuro-fuzzy networks. Systems Science. ISSN 0137-1223. Vol. 28, N.º 1 (2002), pp. 45-57 0137-1223 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Oficyna Wydawnicza Politechniki Wrocławskiej |
publisher.none.fl_str_mv |
Oficyna Wydawnicza Politechniki Wrocławskiej |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133457866031104 |