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Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive Cover

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Power Electronics and Drives
Volume 6 (2021): Issue 1 (January 2021)
By: Kamila Jankowska and  Pawel Ewert  
Open Access
|Jul 2021

Authors

Kamila Jankowska

Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Poland

Pawel Ewert

pawel.ewert@pwr.edu.pl

Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Poland
DOI: https://doi.org/10.2478/pead-2021-0008 | Journal eISSN: 2543-4292 | Journal ISSN: 2451-0262
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Language: English
Page range: 100 - 112
Submitted on: May 14, 2021
Accepted on: Jul 1, 2021
Published on: Jul 23, 2021
Published by: Wroclaw University of Science and Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
PMSM,
rolling bearings,
electric drive diagnostics,
self-organising maps,
shallow neural network
Related subjects:
Computer sciences,
Artificial intelligence,
Engineering,
Electrical engineering,
Electronics

© 2021 Kamila Jankowska, Pawel Ewert, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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