Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912 |
Resumo: | This paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN) is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented. |
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Acta scientiarum. Technology (Online) |
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Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912rigid balancingrotor balancingartificial neural networkrigid balancingrotor balancingartificial neural networkThis paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN) is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented.This paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN) is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented.Universidade Estadual De Maringá2009-06-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/391210.4025/actascitechnol.v31i2.3912Acta Scientiarum. Technology; Vol 31 No 2 (2009); 151-157Acta Scientiarum. Technology; v. 31 n. 2 (2009); 151-1571806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912/3912http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912/5262Santos, Fábio LúcioDuarte, Maria Lúcia MachadoFaria, Marcos Túlio Corrêa deEduardo, Alexandre Carlosinfo:eu-repo/semantics/openAccess2024-05-17T13:03:01Zoai:periodicos.uem.br/ojs:article/3912Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:03:01Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
title |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
spellingShingle |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 Santos, Fábio Lúcio rigid balancing rotor balancing artificial neural network rigid balancing rotor balancing artificial neural network |
title_short |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
title_full |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
title_fullStr |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
title_full_unstemmed |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
title_sort |
Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912 |
author |
Santos, Fábio Lúcio |
author_facet |
Santos, Fábio Lúcio Duarte, Maria Lúcia Machado Faria, Marcos Túlio Corrêa de Eduardo, Alexandre Carlos |
author_role |
author |
author2 |
Duarte, Maria Lúcia Machado Faria, Marcos Túlio Corrêa de Eduardo, Alexandre Carlos |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, Fábio Lúcio Duarte, Maria Lúcia Machado Faria, Marcos Túlio Corrêa de Eduardo, Alexandre Carlos |
dc.subject.por.fl_str_mv |
rigid balancing rotor balancing artificial neural network rigid balancing rotor balancing artificial neural network |
topic |
rigid balancing rotor balancing artificial neural network rigid balancing rotor balancing artificial neural network |
description |
This paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN) is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-06-17 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912 10.4025/actascitechnol.v31i2.3912 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912 |
identifier_str_mv |
10.4025/actascitechnol.v31i2.3912 |
dc.language.iso.fl_str_mv |
por eng |
language |
por eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912/3912 http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3912/5262 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 31 No 2 (2009); 151-157 Acta Scientiarum. Technology; v. 31 n. 2 (2009); 151-157 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315333163515904 |