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Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal

2021Artigo científico Acesso aberto
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dc.contributor.authorBressan, Glaucia
dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorSantos, Herman Lucas dos
dc.contributor.authorEndo, Wagner
dc.contributor.authorAgulhari, Cristiano
dc.contributor.authorGoedtel, Alessandro
dc.contributor.authorScalassara, Paulo
dc.date.accessioned2022-01-13T15:16:28Z
dc.date.available2022-01-13T15:16:28Z
dc.date.issued2021
dc.description.abstractConsidering 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBressan, 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. 353-364pt_PT
dc.identifier.doi10.18576/amis/150313pt_PT
dc.identifier.issn1935-0090
dc.identifier.urihttp://hdl.handle.net/10198/24632
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBayesian networks topologiespt_PT
dc.subjectSupervised learning methodspt_PT
dc.subjectFaults classificationpt_PT
dc.subjectAudio signalspt_PT
dc.titleBayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage364pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage353pt_PT
oaire.citation.titleApplied Mathematics & Information Sciencespt_PT
oaire.citation.volume15pt_PT
person.familyNameAzevedo
person.givenNameBeatriz Flamia
person.identifier.ciencia-id181E-855C-E62C
person.identifier.orcid0000-0002-8527-7409
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isAuthorOfPublication.latestForDiscovery04fa4023-3726-4dd5-8d97-f6b162ceb820

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