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

Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is a...

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Autores principales:	Jankowska Kamila, Ewert Pawel
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2021
Materias:	pmsm rolling bearings electric drive diagnostics self-organising maps shallow neural network Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/d463cfde116f40119e6fed6b8b52c17e
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id	oai:doaj.org-article:d463cfde116f40119e6fed6b8b52c17e
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spelling	oai:doaj.org-article:d463cfde116f40119e6fed6b8b52c17e2021-12-05T14:11:09ZEffectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive2543-429210.2478/pead-2021-0008https://doaj.org/article/d463cfde116f40119e6fed6b8b52c17e2021-01-01T00:00:00Zhttps://doi.org/10.2478/pead-2021-0008https://doaj.org/toc/2543-4292Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.Jankowska KamilaEwert PawelSciendoarticlepmsmrolling bearingselectric drive diagnosticsself-organising mapsshallow neural networkElectronicsTK7800-8360ENPower Electronics and Drives, Vol 6, Iss 1, Pp 100-112 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pmsm rolling bearings electric drive diagnostics self-organising maps shallow neural network Electronics TK7800-8360
spellingShingle	pmsm rolling bearings electric drive diagnostics self-organising maps shallow neural network Electronics TK7800-8360 Jankowska Kamila Ewert Pawel Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
description	Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.
format	article
author	Jankowska Kamila Ewert Pawel
author_facet	Jankowska Kamila Ewert Pawel
author_sort	Jankowska Kamila
title	Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
title_short	Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
title_full	Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
title_fullStr	Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
title_full_unstemmed	Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
title_sort	effectiveness analysis of rolling bearing fault detectors based on self-organising kohonen neural network – a case study of pmsm drive
publisher	Sciendo
publishDate	2021
url	https://doaj.org/article/d463cfde116f40119e6fed6b8b52c17e
work_keys_str_mv	AT jankowskakamila effectivenessanalysisofrollingbearingfaultdetectorsbasedonselforganisingkohonenneuralnetworkacasestudyofpmsmdrive AT ewertpawel effectivenessanalysisofrollingbearingfaultdetectorsbasedonselforganisingkohonenneuralnetworkacasestudyofpmsmdrive
_version_	1718371326222139392

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

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