Detection of the Interturn Shorts of a Three-Phase Motor Using Artificial Intelligence Processing Vibration Data

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dc.contributor"European Union (EU)" & "Horizon 2020"
dc.contributor.authorDoseděl, Martincs
dc.contributor.authorKopečný, Ladislavcs
dc.contributor.authorKozovský, Matúšcs
dc.contributor.authorHnidka, Jakubcs
dc.contributor.authorHavránek, Zdeněkcs
dc.date.issued2022-12-07cs
dc.description.abstractThe paper deals with description, design, learning and inference process of a convolutional 2D neural network for detection of shortened winding turns of a three-phase permanent magnet synchronous motor. Input datasets for aforementioned procedures have been created by sensing vibration data on the real motor using accelerometers with a possibility of artificially induce short circuit in the motor winding. Only simple pre-processing of a time signal has been done – the time waveform was reshaped into 2D greyscale images with a size of 64 x 64 points and led directly into the neural network. No pretrained network has been used – internal parameters have been learned from scratch. Learning process as well as inference of the network have been performed on a standard personal computer with nVidia GeForce RTX 2080 Ti graphics card and implemented usign Python in Keras/TensorFlow environment. Datasets for different working states of the motor, such as speed, torque, error type and its severity have been used. Training procedure of the network has been done within lower tens of minutes and final validation accuracy was 100 % in the most cases, while classification accuracy during inference process has reached the value of more than 99 %. Obtained results confirmed a fact, that faults’ detection of the mechatronic system based on sensing of mechanical quantities and their evaluation is very reliable even in the case of electrical-based faults.en
dc.formattextcs
dc.format.extent234-238cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationProceedings of the 2022 20th International Conference on Mechatronics - Mechatronika (ME). 2022, p. 234-238.en
dc.identifier.doi10.1109/ME54704.2022.9982824cs
dc.identifier.isbn978-1-6654-1040-3cs
dc.identifier.orcid0000-0002-3170-6423cs
dc.identifier.orcid0000-0003-4927-1875cs
dc.identifier.orcid0000-0002-1547-1003cs
dc.identifier.orcid0000-0003-1712-079Xcs
dc.identifier.orcid0000-0002-8761-6590cs
dc.identifier.other182461cs
dc.identifier.researcheridU-6961-2018cs
dc.identifier.researcheridE-2371-2018cs
dc.identifier.researcheridD-7933-2012cs
dc.identifier.scopus54981039700cs
dc.identifier.urihttp://hdl.handle.net/11012/209124
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofProceedings of the 2022 20th International Conference on Mechatronics - Mechatronika (ME)cs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/857306/EU//RICAIP
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/826060/EU//AI4DI
dc.relation.urihttps://ieeexplore.ieee.org/document/9982824cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectneural networken
dc.subjectartificial intelligenceen
dc.subjectCNNen
dc.subjectclassificationen
dc.subjectvibrodiagnosticsen
dc.subjectPMSMen
dc.subjectinterturn short-circuiten
dc.subjectKerasen
dc.subjectTensorFlowen
dc.subjectPythonen
dc.titleDetection of the Interturn Shorts of a Three-Phase Motor Using Artificial Intelligence Processing Vibration Dataen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-182461en
sync.item.dbtypeVAVen
sync.item.insts2024.03.01 18:46:04en
sync.item.modts2024.03.01 18:13:42en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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