An alternative approach to estimate load torque in industrial environment using neural networks

Detalhes bibliográficos
Autor(a) principal: Goedtel, A.
Data de Publicação: 2006
Outros Autores: Silva, I. N. da, Serni, P. J. A. [UNESP], Flauzino, R. A. [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/197375
Resumo: The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.
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spelling An alternative approach to estimate load torque in industrial environment using neural networksThe induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Sao Paulo, Sch Engn, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, BrazilState Univ Sao Paulo, Engn Sch Bauru, BR-17033 Sao Paulo, BrazilState Univ Sao Paulo, Engn Sch Bauru, BR-17033 Sao Paulo, BrazilFAPESP: 03/11353-0IeeeUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Goedtel, A.Silva, I. N. daSerni, P. J. A. [UNESP]Flauzino, R. A. [UNESP]IEEE2020-12-10T22:01:24Z2020-12-10T22:01:24Z2006-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1388-+2006 1st Ieee Conference On Industrial Electronics And Applications, Vols 1-3. New York: Ieee, p. 1388-+, 2006.http://hdl.handle.net/11449/197375WOS:000243884000269Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2006 1st Ieee Conference On Industrial Electronics And Applications, Vols 1-3info:eu-repo/semantics/openAccess2021-10-23T10:18:32Zoai:repositorio.unesp.br:11449/197375Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:26:18.465399Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv An alternative approach to estimate load torque in industrial environment using neural networks
title An alternative approach to estimate load torque in industrial environment using neural networks
spellingShingle An alternative approach to estimate load torque in industrial environment using neural networks
Goedtel, A.
title_short An alternative approach to estimate load torque in industrial environment using neural networks
title_full An alternative approach to estimate load torque in industrial environment using neural networks
title_fullStr An alternative approach to estimate load torque in industrial environment using neural networks
title_full_unstemmed An alternative approach to estimate load torque in industrial environment using neural networks
title_sort An alternative approach to estimate load torque in industrial environment using neural networks
author Goedtel, A.
author_facet Goedtel, A.
Silva, I. N. da
Serni, P. J. A. [UNESP]
Flauzino, R. A. [UNESP]
IEEE
author_role author
author2 Silva, I. N. da
Serni, P. J. A. [UNESP]
Flauzino, R. A. [UNESP]
IEEE
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Goedtel, A.
Silva, I. N. da
Serni, P. J. A. [UNESP]
Flauzino, R. A. [UNESP]
IEEE
description The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.
publishDate 2006
dc.date.none.fl_str_mv 2006-01-01
2020-12-10T22:01:24Z
2020-12-10T22:01:24Z
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 2006 1st Ieee Conference On Industrial Electronics And Applications, Vols 1-3. New York: Ieee, p. 1388-+, 2006.
http://hdl.handle.net/11449/197375
WOS:000243884000269
identifier_str_mv 2006 1st Ieee Conference On Industrial Electronics And Applications, Vols 1-3. New York: Ieee, p. 1388-+, 2006.
WOS:000243884000269
url http://hdl.handle.net/11449/197375
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2006 1st Ieee Conference On Industrial Electronics And Applications, Vols 1-3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1388-+
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
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
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