Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/24632 |
Resumo: | Considering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications. |
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Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic SignalBayesian networks topologiesSupervised learning methodsFaults classificationAudio signalsConsidering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications.Biblioteca Digital do IPBBressan, GlauciaAzevedo, Beatriz FlamiaSantos, Herman Lucas dosEndo, WagnerAgulhari, CristianoGoedtel, AlessandroScalassara, Paulo2022-01-13T15:16:28Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/24632engBressan, Glaucia; Azevedo, Beatriz Flamia; Santos, Herman Lucas dos; Endo, Wagner; Agulhari, Cristiano; Goedtel, Alessandro; Scalassara, Paulo (2021). Bayesian approach to infer types of faults on electrical machines from acoustic signal. Applied Mathematics & Information Sciences. ISSN 1935-0090. 15:3, p. 353-3641935-009010.18576/amis/150313info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:55:34Zoai:bibliotecadigital.ipb.pt:10198/24632Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:15:36.932329Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
title |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
spellingShingle |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal Bressan, Glaucia Bayesian networks topologies Supervised learning methods Faults classification Audio signals |
title_short |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
title_full |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
title_fullStr |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
title_full_unstemmed |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
title_sort |
Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal |
author |
Bressan, Glaucia |
author_facet |
Bressan, Glaucia Azevedo, Beatriz Flamia Santos, Herman Lucas dos Endo, Wagner Agulhari, Cristiano Goedtel, Alessandro Scalassara, Paulo |
author_role |
author |
author2 |
Azevedo, Beatriz Flamia Santos, Herman Lucas dos Endo, Wagner Agulhari, Cristiano Goedtel, Alessandro Scalassara, Paulo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Bressan, Glaucia Azevedo, Beatriz Flamia Santos, Herman Lucas dos Endo, Wagner Agulhari, Cristiano Goedtel, Alessandro Scalassara, Paulo |
dc.subject.por.fl_str_mv |
Bayesian networks topologies Supervised learning methods Faults classification Audio signals |
topic |
Bayesian networks topologies Supervised learning methods Faults classification Audio signals |
description |
Considering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z 2022-01-13T15:16:28Z |
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://hdl.handle.net/10198/24632 |
url |
http://hdl.handle.net/10198/24632 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Bressan, Glaucia; Azevedo, Beatriz Flamia; Santos, Herman Lucas dos; Endo, Wagner; Agulhari, Cristiano; Goedtel, Alessandro; Scalassara, Paulo (2021). Bayesian approach to infer types of faults on electrical machines from acoustic signal. Applied Mathematics & Information Sciences. ISSN 1935-0090. 15:3, p. 353-364 1935-0090 10.18576/amis/150313 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135438902919168 |