Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/161516 |
Resumo: | Early detection of faults in the bearings of electric motors is vital to reduce maintenance costs of industrial motors. Vibration signal analysis is a well-known and widely used diagnostic approach for bearing fault identification, and usually leads to good results in terms of effectiveness and detection capability. However, small defects, at an early stage of development, can be hard to find and require advanced signal processing techniques to facilitate the extraction of the fault characteristic frequencies from the noisy vibration signals. This work compares three different techniques applied to vibration signals to facilitate the extraction of the fault frequency components, namely the Teager-Kaiser operator, discrete wavelet transform and the Hilbert transform. A test bench was built and several types of defects were introduced in the motor bearings to compare vibration signals obtained with a healthy and a faulty motor. Comparative graphs of the results obtained with the three techniques are presented and the results are discussed. |
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Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing ApproachesBearing faultsdiagnosticsdiscrete waveletsHilbert transformTeager-Kaiser Operatorinduction motorEarly detection of faults in the bearings of electric motors is vital to reduce maintenance costs of industrial motors. Vibration signal analysis is a well-known and widely used diagnostic approach for bearing fault identification, and usually leads to good results in terms of effectiveness and detection capability. However, small defects, at an early stage of development, can be hard to find and require advanced signal processing techniques to facilitate the extraction of the fault characteristic frequencies from the noisy vibration signals. This work compares three different techniques applied to vibration signals to facilitate the extraction of the fault frequency components, namely the Teager-Kaiser operator, discrete wavelet transform and the Hilbert transform. A test bench was built and several types of defects were introduced in the motor bearings to compare vibration signals obtained with a healthy and a faulty motor. Comparative graphs of the results obtained with the three techniques are presented and the results are discussed.Univ Coimbra, Inst Telecomunicacoes, Dept Elect & Comp Engn, Coimbra, PortugalUniv Estadual Paulista UNESP, Sch Engn, Dept Elect Engn, Bauru, BrazilUniv Estadual Paulista UNESP, Sch Engn, Dept Elect Engn, Bauru, BrazilIeeeUniv CoimbraUniversidade Estadual Paulista (Unesp)Goncalves, Mario J. M.Creppe, Renato C. [UNESP]Marques, Emanuel G.Cruz, Sergio M. A.IEEE2018-11-26T16:33:03Z2018-11-26T16:33:03Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject488-4932015 Ieee 24th International Symposium On Industrial Electronics (isie). New York: Ieee, p. 488-493, 2015.2163-5137http://hdl.handle.net/11449/161516WOS:000376164100073Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee 24th International Symposium On Industrial Electronics (isie)info:eu-repo/semantics/openAccess2024-06-28T13:34:42Zoai:repositorio.unesp.br:11449/161516Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:46:41.983010Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
title |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
spellingShingle |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches Goncalves, Mario J. M. Bearing faults diagnostics discrete wavelets Hilbert transform Teager-Kaiser Operator induction motor |
title_short |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
title_full |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
title_fullStr |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
title_full_unstemmed |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
title_sort |
Diagnosis of Bearing Faults in Induction Motors By Vibration Signals - Comparison of Multiple Signal Processing Approaches |
author |
Goncalves, Mario J. M. |
author_facet |
Goncalves, Mario J. M. Creppe, Renato C. [UNESP] Marques, Emanuel G. Cruz, Sergio M. A. IEEE |
author_role |
author |
author2 |
Creppe, Renato C. [UNESP] Marques, Emanuel G. Cruz, Sergio M. A. IEEE |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Univ Coimbra Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Goncalves, Mario J. M. Creppe, Renato C. [UNESP] Marques, Emanuel G. Cruz, Sergio M. A. IEEE |
dc.subject.por.fl_str_mv |
Bearing faults diagnostics discrete wavelets Hilbert transform Teager-Kaiser Operator induction motor |
topic |
Bearing faults diagnostics discrete wavelets Hilbert transform Teager-Kaiser Operator induction motor |
description |
Early detection of faults in the bearings of electric motors is vital to reduce maintenance costs of industrial motors. Vibration signal analysis is a well-known and widely used diagnostic approach for bearing fault identification, and usually leads to good results in terms of effectiveness and detection capability. However, small defects, at an early stage of development, can be hard to find and require advanced signal processing techniques to facilitate the extraction of the fault characteristic frequencies from the noisy vibration signals. This work compares three different techniques applied to vibration signals to facilitate the extraction of the fault frequency components, namely the Teager-Kaiser operator, discrete wavelet transform and the Hilbert transform. A test bench was built and several types of defects were introduced in the motor bearings to compare vibration signals obtained with a healthy and a faulty motor. Comparative graphs of the results obtained with the three techniques are presented and the results are discussed. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2018-11-26T16:33:03Z 2018-11-26T16:33:03Z |
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 |
2015 Ieee 24th International Symposium On Industrial Electronics (isie). New York: Ieee, p. 488-493, 2015. 2163-5137 http://hdl.handle.net/11449/161516 WOS:000376164100073 |
identifier_str_mv |
2015 Ieee 24th International Symposium On Industrial Electronics (isie). New York: Ieee, p. 488-493, 2015. 2163-5137 WOS:000376164100073 |
url |
http://hdl.handle.net/11449/161516 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2015 Ieee 24th International Symposium On Industrial Electronics (isie) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
488-493 |
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 |
|
_version_ |
1808129247247597568 |