Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

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dc.contributor.authorZuth, Danielcs
dc.contributor.authorBlecha, Petrcs
dc.contributor.authorMarada, Tomášcs
dc.contributor.authorHuzlík, Rostislavcs
dc.contributor.authorTůma, Jiřícs
dc.contributor.authorMaradová, Karlacs
dc.contributor.authorFrkal, Vojtěchcs
dc.coverage.issue10cs
dc.coverage.volume9cs
dc.date.accessioned2021-12-15T15:55:17Z
dc.date.available2021-12-15T15:55:17Z
dc.date.issued2021-09-30cs
dc.description.abstractThe current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.</p>en
dc.formattextcs
dc.format.extent1-19cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMachines. 2021, vol. 9, issue 10, p. 1-19.en
dc.identifier.doi10.3390/machines9100222cs
dc.identifier.issn2075-1702cs
dc.identifier.other175274cs
dc.identifier.urihttp://hdl.handle.net/11012/203230
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofMachinescs
dc.relation.urihttps://www.mdpi.com/2075-1702/9/10/222cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2075-1702/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectvibrodiagnosticsen
dc.subjectclassification learner appen
dc.subjectmachine learningen
dc.subjectMATLABen
dc.subjectPythonen
dc.subjectclassification modelen
dc.subjectunbalanceen
dc.titleVibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machineryen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-175274en
sync.item.dbtypeVAVen
sync.item.insts2022.05.30 12:51:59en
sync.item.modts2022.05.30 12:14:11en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav výrobních strojů, systémů a robotikycs
thesis.grantorVysoké učení technické v Brně. . TOSHULIN, a.s.cs
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Ústav výrobních strojů, systémů a robotiky