Implementation of ANN for PMSM interturn short-circuit detection in the embedded system

Simple item page
dc.contributor.authorKozovský, Matúšcs
dc.contributor.authorBuchta, Luděkcs
dc.contributor.authorBlaha, Petrcs
dc.date.issued2023-10-16cs
dc.description.abstractThe problem of condition monitoring and fault detection in powertrain systems becomes more critical with the increasing use of fail-operational systems. These systems are essential in the automotive industry, robotics, and other industrial applications. One of the critical features of such a system is recognizing the fault and suppressing its influence. The paper describes a feed-forward artificial neural network-based diagnostic of interturn short-circuit faults in a dual three-phase permanent magnet synchronous motor. The paper focuses on using MLPN, and CNN for interturn short-circuit detection and, more importantly, their real implementation into the automotive AURIX TC397 microcontroller. The paper presents the achieved neural network inference times as well as data preprocessing computation time. The behavior of the ANNs is tested on an experimental configurable multiphase PMSM drive with the possibility to emulate interturn short-circuit fault using prepared winding taps. The paper includes the essential aspects that should be respected during ANN design and implementation into the microcontroller.en
dc.formattextcs
dc.format.extent1-6cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. 2023, p. 1-6.en
dc.identifier.doi10.1109/IECON51785.2023.10312642cs
dc.identifier.isbn979-8-3503-3182-0cs
dc.identifier.orcid0000-0002-1547-1003cs
dc.identifier.orcid0000-0002-8954-3495cs
dc.identifier.orcid0000-0001-5534-2065cs
dc.identifier.other185461cs
dc.identifier.researcheridE-2371-2018cs
dc.identifier.researcheridG-8085-2014cs
dc.identifier.researcheridD-6854-2012cs
dc.identifier.scopus56028720700cs
dc.identifier.scopus7006825993cs
dc.identifier.urihttp://hdl.handle.net/11012/245226
dc.language.isoencs
dc.publisherIEEEcs
dc.relationEuropean Union (EU) & "Horizon 2020"
dc.relation.ispartofIECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Societycs
dc.relation.projectIdRICAIP info:eu-repo/grantAgreement/EC/H2020/857306/EU//RICAIP
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/101007326/EU//AI4CSM
dc.relation.urihttps://ieeexplore.ieee.org/document/10312642cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectNeural networken
dc.subjectfault detectionen
dc.subjectdiagnosticen
dc.subjectPMSMen
dc.subjectmotoren
dc.titleImplementation of ANN for PMSM interturn short-circuit detection in the embedded systemen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-185461en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:50:44en
sync.item.modts2025.01.17 15:19:02en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
IECON51785.2023.10312642accepted.pdf
Size:
1.12 MB
Format:
Adobe Portable Document Format
Description:
file IECON51785.2023.10312642accepted.pdf
Download
Collections
Kybernetika a robotika