Publication detail

INTER TURN SHORT-CIRCUIT DETECTION IN VECTOR CONTROLLED PMS MOTOR USING AI

ZEZULA, L.

Original Title

INTER TURN SHORT-CIRCUIT DETECTION IN VECTOR CONTROLLED PMS MOTOR USING AI

English Title

INTER TURN SHORT-CIRCUIT DETECTION IN VECTOR CONTROLLED PMS MOTOR USING AI

Type

article in a collection out of WoS and Scopus

Language

Czech

Original Abstract

This paper deals with the diagnostics of inter turn faults in a vector controlled synchronous motor with permanent magnets. Inter turn faults are detected by a convolution neural network from adequately preprocessed current signals of the stator phases. The goal is to create a model within which different severity of inter turn faults will be simulated. Data from the simulations are preprocessed and transformed using Wavelet transform and the resulting scalograms are fed to a pre-trained convolution neural network GoogLeNet. This neural network’s diagnostic capabilities are tested on a physical drive, capable of emulating faults.

English abstract

This paper deals with the diagnostics of inter turn faults in a vector controlled synchronous motor with permanent magnets. Inter turn faults are detected by a convolution neural network from adequately preprocessed current signals of the stator phases. The goal is to create a model within which different severity of inter turn faults will be simulated. Data from the simulations are preprocessed and transformed using Wavelet transform and the resulting scalograms are fed to a pre-trained convolution neural network GoogLeNet. This neural network’s diagnostic capabilities are tested on a physical drive, capable of emulating faults.

Keywords

PMSM, ITF, Inter turn fault, Inter turn short-circuit, Vector control, Convolutional neural network, Motor fault diagnostics

Key words in English

PMSM, ITF, Inter turn fault, Inter turn short-circuit, Vector control, Convolutional neural network, Motor fault diagnostics

Authors

ZEZULA, L.

Released

23. 4. 2020

ISBN

978-80-214-5867-3

Book

Proceedings I of the 26th Conference STUDENT EEICT 2020

Edition number

1

Pages from

63

Pages to

66

Pages count

4

BibTex

@inproceedings{BUT179036,
  author="Lukáš {Zezula}",
  title="INTER TURN SHORT-CIRCUIT DETECTION IN VECTOR CONTROLLED PMS MOTOR USING AI",
  booktitle="Proceedings I of the 26th Conference STUDENT EEICT 2020",
  year="2020",
  number="1",
  pages="63--66",
  isbn="978-80-214-5867-3"
}