Using Artificial Intelligence to Determine the Type of Rotary Machine Fault

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dc.contributor.authorZuth, Daniel
dc.contributor.authorMarada, Tomas
dc.coverage.issue2cs
dc.coverage.volume24cs
dc.date.accessioned2019-06-27T06:12:50Z
dc.date.available2019-06-27T06:12:50Z
dc.date.issued2018-12-21cs
dc.description.abstractThe article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classication learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.en
dc.formattextcs
dc.format.extent49–54cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2018 vol. 24, č. 2, s. 49–54. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2018.2.049en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/179248
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/10cs
dc.rights.accessopenAccessen
dc.subjectVibrodiagnosticsen
dc.subjectNeuron Networken
dc.subjectClassification Learneren
dc.subjectMachine Learningen
dc.subjectMatlaben
dc.subjectIndustry 4.0en
dc.subjectClassification Methoden
dc.subjectStatic Unbalanceen
dc.subjectDynamic Unbalanceen
dc.titleUsing Artificial Intelligence to Determine the Type of Rotary Machine Faulten
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 24, No. 2