Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux

In industrial drive systems, one of the widest group of machines are induction motors. During normal operation, these machines are exposed to various types of damages, resulting in high economic losses. Electrical circuits damages are more than half of all damages appearing in induction motors. In c...

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Main Author: M. Skowron
Format: Article
Language:English
Published: Polish Academy of Sciences 2020-10-01
Series:Bulletin of the Polish Academy of Sciences: Technical Sciences
Subjects:
Online Access:https://journals.pan.pl/Content/117697/PDF/08_D1031-1038_01555_Bpast.No.68-5_30.10.20_.pdf
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author M. Skowron
author_facet M. Skowron
author_sort M. Skowron
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description In industrial drive systems, one of the widest group of machines are induction motors. During normal operation, these machines are exposed to various types of damages, resulting in high economic losses. Electrical circuits damages are more than half of all damages appearing in induction motors. In connection with the above, the task of early detection of machine defects becomes a priority in modern drive systems. The article presents the possibility of using deep neural networks to detect stator and rotor damages. The opportunity of detecting shorted turns and the broken rotor bars with the use of an axial flux signal is presented.
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spelling doaj.art-a0333d551c8b4838b9b9228f6795d4092022-12-22T04:01:34ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172020-10-0168No. 5 (i.a. Special Section on Modern control of drives and power converters)10311038https://doi.org/10.24425/bpasts.2020.134664Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial fluxM. SkowronIn industrial drive systems, one of the widest group of machines are induction motors. During normal operation, these machines are exposed to various types of damages, resulting in high economic losses. Electrical circuits damages are more than half of all damages appearing in induction motors. In connection with the above, the task of early detection of machine defects becomes a priority in modern drive systems. The article presents the possibility of using deep neural networks to detect stator and rotor damages. The opportunity of detecting shorted turns and the broken rotor bars with the use of an axial flux signal is presented.https://journals.pan.pl/Content/117697/PDF/08_D1031-1038_01555_Bpast.No.68-5_30.10.20_.pdfinduction motoraxial fluxdeep learningconvolutional neural networks
spellingShingle M. Skowron
Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
Bulletin of the Polish Academy of Sciences: Technical Sciences
induction motor
axial flux
deep learning
convolutional neural networks
title Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
title_full Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
title_fullStr Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
title_full_unstemmed Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
title_short Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
title_sort application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
topic induction motor
axial flux
deep learning
convolutional neural networks
url https://journals.pan.pl/Content/117697/PDF/08_D1031-1038_01555_Bpast.No.68-5_30.10.20_.pdf
work_keys_str_mv AT mskowron applicationofdeeplearningneuralnetworksforthediagnosisofelectricaldamagetotheinductionmotorusingtheaxialflux