Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s40430-020-02554-5 http://hdl.handle.net/11449/199267 |
Resumo: | The functional-series angle-/time-varying autoregressive moving-average (AT-FS-ARMA) model was used to model and analyze vibration-based signals from internal combustion engines. This approach is derived from the formulation of the time–angle periodically correlated processes, a relatively new topic in the cyclostationary framework, which has gained attention for modeling of mechanical signals. The AT-FS-ARMA model consists of traditional time-varying FS-ARMA-like models, but with the projection coefficients expanded in terms of the angular variable, dependent on time. Therefore, the method has the advantage of considering the angle periodicities often present in vibration-based signals from rotating and reciprocating machines. The performance is illustrated by an experimental application of signals measured in a diesel internal combustion engine (ICE) with a constant operating speed. The accuracy of the model is evaluated through the residual sum of squares normalized by the series sum of squares. To illustrate the use of the AT-FS-ARMA for vibration analysis of ICEs, parametric angle–frequency spectrum was estimated and compared to angular-varying pseudo-Wigner–Ville distribution/spectrum. The results showed that AT-FS-ARMA provides a useful complementary tool for analysis. |
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Repositório Institucional da UNESP |
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Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domainAngle-/time-varying ARMA modelsCyclostationarityInternal combustion diesel engineRotating machines and/or reciprocating machinesSystem identificationThe functional-series angle-/time-varying autoregressive moving-average (AT-FS-ARMA) model was used to model and analyze vibration-based signals from internal combustion engines. This approach is derived from the formulation of the time–angle periodically correlated processes, a relatively new topic in the cyclostationary framework, which has gained attention for modeling of mechanical signals. The AT-FS-ARMA model consists of traditional time-varying FS-ARMA-like models, but with the projection coefficients expanded in terms of the angular variable, dependent on time. Therefore, the method has the advantage of considering the angle periodicities often present in vibration-based signals from rotating and reciprocating machines. The performance is illustrated by an experimental application of signals measured in a diesel internal combustion engine (ICE) with a constant operating speed. The accuracy of the model is evaluated through the residual sum of squares normalized by the series sum of squares. To illustrate the use of the AT-FS-ARMA for vibration analysis of ICEs, parametric angle–frequency spectrum was estimated and compared to angular-varying pseudo-Wigner–Ville distribution/spectrum. The results showed that AT-FS-ARMA provides a useful complementary tool for analysis.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Departamento de Engenharia Mecânica UNESP - Universidade Estadual PaulistaINSA-Lyon Laboratoire Vibrations Acoustique Univ LyonDepartamento de Engenharia Mecânica UNESP - Universidade Estadual PaulistaCNPq: 306526/2019-0CAPES: Capes/PDSE/Process n. 88881.189237/2018-01CAPES: Finance Code 001Universidade Estadual Paulista (Unesp)Univ LyonBicudo Jambersi, Andreyson [UNESP]da Silva, Samuel [UNESP]Antoni, Jérôme2020-12-12T01:35:11Z2020-12-12T01:35:11Z2020-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s40430-020-02554-5Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 9, 2020.1806-36911678-5878http://hdl.handle.net/11449/19926710.1007/s40430-020-02554-52-s2.0-85089501205Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Brazilian Society of Mechanical Sciences and Engineeringinfo:eu-repo/semantics/openAccess2021-10-23T06:37:14Zoai:repositorio.unesp.br:11449/199267Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T06:37:14Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
title |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
spellingShingle |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain Bicudo Jambersi, Andreyson [UNESP] Angle-/time-varying ARMA models Cyclostationarity Internal combustion diesel engine Rotating machines and/or reciprocating machines System identification |
title_short |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
title_full |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
title_fullStr |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
title_full_unstemmed |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
title_sort |
Data-driven identification of rotating machines using ARMA deterministic parameter evolution in the angle/time domain |
author |
Bicudo Jambersi, Andreyson [UNESP] |
author_facet |
Bicudo Jambersi, Andreyson [UNESP] da Silva, Samuel [UNESP] Antoni, Jérôme |
author_role |
author |
author2 |
da Silva, Samuel [UNESP] Antoni, Jérôme |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Univ Lyon |
dc.contributor.author.fl_str_mv |
Bicudo Jambersi, Andreyson [UNESP] da Silva, Samuel [UNESP] Antoni, Jérôme |
dc.subject.por.fl_str_mv |
Angle-/time-varying ARMA models Cyclostationarity Internal combustion diesel engine Rotating machines and/or reciprocating machines System identification |
topic |
Angle-/time-varying ARMA models Cyclostationarity Internal combustion diesel engine Rotating machines and/or reciprocating machines System identification |
description |
The functional-series angle-/time-varying autoregressive moving-average (AT-FS-ARMA) model was used to model and analyze vibration-based signals from internal combustion engines. This approach is derived from the formulation of the time–angle periodically correlated processes, a relatively new topic in the cyclostationary framework, which has gained attention for modeling of mechanical signals. The AT-FS-ARMA model consists of traditional time-varying FS-ARMA-like models, but with the projection coefficients expanded in terms of the angular variable, dependent on time. Therefore, the method has the advantage of considering the angle periodicities often present in vibration-based signals from rotating and reciprocating machines. The performance is illustrated by an experimental application of signals measured in a diesel internal combustion engine (ICE) with a constant operating speed. The accuracy of the model is evaluated through the residual sum of squares normalized by the series sum of squares. To illustrate the use of the AT-FS-ARMA for vibration analysis of ICEs, parametric angle–frequency spectrum was estimated and compared to angular-varying pseudo-Wigner–Ville distribution/spectrum. The results showed that AT-FS-ARMA provides a useful complementary tool for analysis. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:35:11Z 2020-12-12T01:35:11Z 2020-09-01 |
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/10.1007/s40430-020-02554-5 Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 9, 2020. 1806-3691 1678-5878 http://hdl.handle.net/11449/199267 10.1007/s40430-020-02554-5 2-s2.0-85089501205 |
url |
http://dx.doi.org/10.1007/s40430-020-02554-5 http://hdl.handle.net/11449/199267 |
identifier_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 9, 2020. 1806-3691 1678-5878 10.1007/s40430-020-02554-5 2-s2.0-85089501205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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_ |
1799965675391811584 |