Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate

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dc.contributor"European Union (EU)" & "Horizon 2020"en
dc.contributor.authorKlíma, Bohumilcs
dc.contributor.authorBuchta, Luděkcs
dc.contributor.authorDoseděl, Martincs
dc.contributor.authorHavránek, Zdeněkcs
dc.contributor.authorBlaha, Petrcs
dc.date.accessioned2019-11-29T15:54:50Z
dc.date.available2019-11-29T15:54:50Z
dc.date.issued2019-09-10cs
dc.description.abstractImportance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives – especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.en
dc.formattextcs
dc.format.extent870-876cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citation2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2019, p. 870-876.en
dc.identifier.doi10.1109/ETFA.2019.8869520cs
dc.identifier.isbn978-1-7281-0303-7cs
dc.identifier.other159092cs
dc.identifier.urihttp://hdl.handle.net/11012/184081
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartof2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)cs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/737453 /EU//I-MECHen
dc.relation.urihttps://ieeexplore.ieee.org/document/8869520cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectprognosis and health managementen
dc.subjectPHMen
dc.subjectpredictive maintenanceen
dc.subjectelectric driveen
dc.subjectcondition indicatoren
dc.titlePrognosis and Health Management in electric drives applications implemented in existing systems with limited data rateen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-159092en
sync.item.dbtypeVAVen
sync.item.insts2021.03.31 00:56:30en
sync.item.modts2021.03.31 00:14:58en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika pro materiálové vědycs
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