A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory condi...

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Autores principales: Koteleva, Natalia, Korolev, Nikolay, Zhukovskiy, Yuriy, Baranov, Georgii
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659703/
https://www.ncbi.nlm.nih.gov/pubmed/34883902
http://dx.doi.org/10.3390/s21237900
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author Koteleva, Natalia
Korolev, Nikolay
Zhukovskiy, Yuriy
Baranov, Georgii
author_facet Koteleva, Natalia
Korolev, Nikolay
Zhukovskiy, Yuriy
Baranov, Georgii
author_sort Koteleva, Natalia
collection PubMed
description This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.
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spelling pubmed-86597032021-12-10 A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage Koteleva, Natalia Korolev, Nikolay Zhukovskiy, Yuriy Baranov, Georgii Sensors (Basel) Article This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data. MDPI 2021-11-26 /pmc/articles/PMC8659703/ /pubmed/34883902 http://dx.doi.org/10.3390/s21237900 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Koteleva, Natalia
Korolev, Nikolay
Zhukovskiy, Yuriy
Baranov, Georgii
A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title_full A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title_fullStr A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title_full_unstemmed A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title_short A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage
title_sort soft sensor for measuring the wear of an induction motor bearing by the park’s vector components of current and voltage
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659703/
https://www.ncbi.nlm.nih.gov/pubmed/34883902
http://dx.doi.org/10.3390/s21237900
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