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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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2020-10-01
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| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
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| Online Access: | https://journals.pan.pl/Content/117697/PDF/08_D1031-1038_01555_Bpast.No.68-5_30.10.20_.pdf |
| Summary: | 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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| ISSN: | 2300-1917 |