Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1177/1077546314524260 http://hdl.handle.net/11449/158609 |
Resumo: | Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used. |
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Repositório Institucional da UNESP |
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Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptronArtificial Neural NetworkMulti Layer PerceptronCondition-Based Monitoringvibration monitoringRolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used.Univ Taubate, Dept Informat, BR-12010000 Taubate, SP, BrazilUniv Taubate, Dept Elect Engn, BR-12010000 Taubate, SP, BrazilSao Paulo State Univ, Fac Engn, Sao Paulo, BrazilSao Paulo State Univ, Fac Engn, Sao Paulo, BrazilSage Publications LtdUniv TaubateUniversidade Estadual Paulista (Unesp)Almeida, Luis F. deBizarria, Jose W. P.Bizarria, Francisco C. P.Mathias, Mauro H. [UNESP]2018-11-26T15:28:18Z2018-11-26T15:28:18Z2015-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3456-3464application/pdfhttp://dx.doi.org/10.1177/1077546314524260Journal Of Vibration And Control. London: Sage Publications Ltd, v. 21, n. 16, p. 3456-3464, 2015.1077-5463http://hdl.handle.net/11449/15860910.1177/1077546314524260WOS:000365615000025WOS000365615000025.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Vibration And Control0,763info:eu-repo/semantics/openAccess2024-01-10T06:22:46Zoai:repositorio.unesp.br:11449/158609Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-10T06:22:46Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
title |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
spellingShingle |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron Almeida, Luis F. de Artificial Neural Network Multi Layer Perceptron Condition-Based Monitoring vibration monitoring |
title_short |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
title_full |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
title_fullStr |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
title_full_unstemmed |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
title_sort |
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron |
author |
Almeida, Luis F. de |
author_facet |
Almeida, Luis F. de Bizarria, Jose W. P. Bizarria, Francisco C. P. Mathias, Mauro H. [UNESP] |
author_role |
author |
author2 |
Bizarria, Jose W. P. Bizarria, Francisco C. P. Mathias, Mauro H. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Univ Taubate Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Almeida, Luis F. de Bizarria, Jose W. P. Bizarria, Francisco C. P. Mathias, Mauro H. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial Neural Network Multi Layer Perceptron Condition-Based Monitoring vibration monitoring |
topic |
Artificial Neural Network Multi Layer Perceptron Condition-Based Monitoring vibration monitoring |
description |
Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-12-01 2018-11-26T15:28:18Z 2018-11-26T15:28:18Z |
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://dx.doi.org/10.1177/1077546314524260 Journal Of Vibration And Control. London: Sage Publications Ltd, v. 21, n. 16, p. 3456-3464, 2015. 1077-5463 http://hdl.handle.net/11449/158609 10.1177/1077546314524260 WOS:000365615000025 WOS000365615000025.pdf |
url |
http://dx.doi.org/10.1177/1077546314524260 http://hdl.handle.net/11449/158609 |
identifier_str_mv |
Journal Of Vibration And Control. London: Sage Publications Ltd, v. 21, n. 16, p. 3456-3464, 2015. 1077-5463 10.1177/1077546314524260 WOS:000365615000025 WOS000365615000025.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Vibration And Control 0,763 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
3456-3464 application/pdf |
dc.publisher.none.fl_str_mv |
Sage Publications Ltd |
publisher.none.fl_str_mv |
Sage Publications Ltd |
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 |
|
_version_ |
1803047303779450880 |