Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermo...

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Autores principales:	Amine Mahami, Chemseddine Rahmoune, Toufik Bettahar, Djamel Benazzouz
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/33f9873071a04a9f94bfecec7fb594a7
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spelling	oai:doaj.org-article:33f9873071a04a9f94bfecec7fb594a72021-12-02T03:33:59ZInduction motor condition monitoring using infrared thermography imaging and ensemble learning techniques1687-814010.1177/16878140211060956https://doaj.org/article/33f9873071a04a9f94bfecec7fb594a72021-11-01T00:00:00Zhttps://doi.org/10.1177/16878140211060956https://doaj.org/toc/1687-8140In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.Amine MahamiChemseddine RahmouneToufik BettaharDjamel BenazzouzSAGE PublishingarticleMechanical engineering and machineryTJ1-1570ENAdvances in Mechanical Engineering, Vol 13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mechanical engineering and machinery TJ1-1570
spellingShingle	Mechanical engineering and machinery TJ1-1570 Amine Mahami Chemseddine Rahmoune Toufik Bettahar Djamel Benazzouz Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
description	In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.
format	article
author	Amine Mahami Chemseddine Rahmoune Toufik Bettahar Djamel Benazzouz
author_facet	Amine Mahami Chemseddine Rahmoune Toufik Bettahar Djamel Benazzouz
author_sort	Amine Mahami
title	Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
title_short	Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
title_full	Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
title_fullStr	Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
title_full_unstemmed	Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
title_sort	induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/33f9873071a04a9f94bfecec7fb594a7
work_keys_str_mv	AT aminemahami inductionmotorconditionmonitoringusinginfraredthermographyimagingandensemblelearningtechniques AT chemseddinerahmoune inductionmotorconditionmonitoringusinginfraredthermographyimagingandensemblelearningtechniques AT toufikbettahar inductionmotorconditionmonitoringusinginfraredthermographyimagingandensemblelearningtechniques AT djamelbenazzouz inductionmotorconditionmonitoringusinginfraredthermographyimagingandensemblelearningtechniques
_version_	1718401721958400000

Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

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