Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux

[EN] Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector dema...

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Autores: Zamudio-Ramírez, Israel, Osornio-Rios, Roque Alfredo, Trejo-Hernandez, Miguel, Romero-Troncoso, Rene de Jesus, J. Antonino-Daviu|||0000-0003-1898-2228
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/204571
Acceso en línea:https://riunet.upv.es/handle/10251/204571
Access Level:acceso abierto
Palabra clave:Induction motor
Smart-sensor
Stray flux
Time-frequency transforms
Wavelet entropy
INGENIERIA ELECTRICA
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
title Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
spellingShingle Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
Zamudio-Ramírez, Israel
Induction motor
Smart-sensor
Stray flux
Time-frequency transforms
Wavelet entropy
INGENIERIA ELECTRICA
title_short Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
title_full Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
title_fullStr Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
title_full_unstemmed Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
title_sort Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
dc.creator.none.fl_str_mv Zamudio-Ramírez, Israel
Osornio-Rios, Roque Alfredo
Trejo-Hernandez, Miguel
Romero-Troncoso, Rene de Jesus
J. Antonino-Daviu|||0000-0003-1898-2228
author Zamudio-Ramírez, Israel
author_facet Zamudio-Ramírez, Israel
Osornio-Rios, Roque Alfredo
Trejo-Hernandez, Miguel
Romero-Troncoso, Rene de Jesus
J. Antonino-Daviu|||0000-0003-1898-2228
author_role author
author2 Osornio-Rios, Roque Alfredo
Trejo-Hernandez, Miguel
Romero-Troncoso, Rene de Jesus
J. Antonino-Daviu|||0000-0003-1898-2228
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Instituto de Tecnología Eléctrica
Departamento de Ingeniería Eléctrica
Escuela Técnica Superior de Ingeniería Industrial
AGENCIA ESTATAL DE INVESTIGACION
Universidad Autónoma de Querétaro
Consejo Nacional de Humanidades, Ciencias y Tecnologías, México
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Induction motor
Smart-sensor
Stray flux
Time-frequency transforms
Wavelet entropy
INGENIERIA ELECTRICA
topic Induction motor
Smart-sensor
Stray flux
Time-frequency transforms
Wavelet entropy
INGENIERIA ELECTRICA
description [EN] Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-05-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/204571
url https://riunet.upv.es/handle/10251/204571
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PGC2018-095747-B-I00-AR TECNOLOGIAS AVANZADAS BASADAS EN EL ANALISIS DEL FLUJO DE DISPERSION EN REGIMEN TRANSITORIO PARA EL DIAGNOSTICO PRECOZ DE ANOMALIAS ELECTROMECANICAS EN MOTORES ELECTRICOS.
Consejo Nacional de Ciencia y Tecnología, México https://doi.org/10.13039/501100003141 SEP-CONACYT 222453-2013
UAQ UAQ FOFIUAQ-FIN201812
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
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spelling Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray FluxZamudio-Ramírez, IsraelOsornio-Rios, Roque AlfredoTrejo-Hernandez, MiguelRomero-Troncoso, Rene de JesusJ. Antonino-Daviu|||0000-0003-1898-2228Induction motorSmart-sensorStray fluxTime-frequency transformsWavelet entropyINGENIERIA ELECTRICA[EN] Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.We would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACYT) for providing economic support in this work (scholarship). Finally, thanks to the next projects: SEP-CONACYT 222453-2013, and FOFIUAQ-FIN201812. This wok was also funded by Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).MDPI AGInstituto de Tecnología EléctricaDepartamento de Ingeniería EléctricaEscuela Técnica Superior de Ingeniería IndustrialAGENCIA ESTATAL DE INVESTIGACIONUniversidad Autónoma de QuerétaroConsejo Nacional de Humanidades, Ciencias y Tecnologías, MéxicoRepositorio Institucional de la Universitat Politècnica de València Riunet20192019-05-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/204571reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PGC2018-095747-B-I00-AR TECNOLOGIAS AVANZADAS BASADAS EN EL ANALISIS DEL FLUJO DE DISPERSION EN REGIMEN TRANSITORIO PARA EL DIAGNOSTICO PRECOZ DE ANOMALIAS ELECTROMECANICAS EN MOTORES ELECTRICOS.Consejo Nacional de Ciencia y Tecnología, México https://doi.org/10.13039/501100003141 SEP-CONACYT 222453-2013UAQ UAQ FOFIUAQ-FIN201812open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2045712026-06-13T07:49:27Z
score 15,300719

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