Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor

The development of novel condition monitoring strategies represents a critical challenge to ensure the effectiveness and reliability of complex industrial processes. Indeed, the interconnectivity of multiple variables facilitates the data exploitation under the framework of the Industry 4.0 and, sub...

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Autores: Saucedo Dorantes, Juan Jose, Jaen Cuellar, Arturo Yosimar, Delgado Prieto, Miquel|||0000-0001-9282-838X, Romero Troncoso, René de Jesús, Osornio Rios, Roque A.
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/361165
Acceso en línea:https://hdl.handle.net/2117/361165
https://dx.doi.org/10.1016/j.measurement.2021.109404
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Electric motors, Induction
Condition monitoring
Multi-fault diagnosis
Feature selection
Feature reduction
Intel·ligència artificial
Motors elèctrics d'inducció
Àrees temàtiques de la UPC::Enginyeria electrònica
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oai_identifier_str oai:upcommons.upc.edu:2117/361165
network_acronym_str ES
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spelling Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motorSaucedo Dorantes, Juan JoseJaen Cuellar, Arturo YosimarDelgado Prieto, Miquel|||0000-0001-9282-838XRomero Troncoso, René de JesúsOsornio Rios, Roque A.Artificial intelligenceElectric motors, InductionCondition monitoringMulti-fault diagnosisFeature selectionFeature reductionArtificial intelligenceIntel·ligència artificialMotors elèctrics d'induccióÀrees temàtiques de la UPC::Enginyeria electrònicaThe development of novel condition monitoring strategies represents a critical challenge to ensure the effectiveness and reliability of complex industrial processes. Indeed, the interconnectivity of multiple variables facilitates the data exploitation under the framework of the Industry 4.0 and, subsequently, the advanced monitoring may prevent unexpected conditions. Therefore, in this work it is proposed a condition monitoring methodology based on the estimation and optimization of a high-dimensional set of hybrid features for identifying and assessing the occurrence of multiple and combined faults that appear simultaneously in an induction motor. The contribution of this work includes the high-performance characterization of the induction motor operation by means of the high-dimensional set of hybrid features which is estimated from the analysis of vibrations and stator currents through techniques from different domains. Additionally, the validation that by using artificial intelligence and machine learning-based techniques allows the implementation of stages to optimize and reduce the high-dimensional feature space, leading to the selection and retention of the most discriminative features of the considered conditions. Finally, the automated diagnostics of multiple and combined faults, performed by a Neural Network-based classifier, highlights the effectiveness of the proposed method to overcome the occurrence of multiple faults that may appear simultaneously. The proposed method is validated under a complete set of experimental data that includes the healthy condition, three single fault conditions and four combined fault conditions, where the combinations of two and three fault conditions are studied.Peer ReviewedElsevier20212021-06-0120222022-02-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/361165https://dx.doi.org/10.1016/j.measurement.2021.109404reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3611652026-05-27T15:37:01Z
dc.title.none.fl_str_mv Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
title Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
spellingShingle Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
Saucedo Dorantes, Juan Jose
Artificial intelligence
Electric motors, Induction
Condition monitoring
Multi-fault diagnosis
Feature selection
Feature reduction
Artificial intelligence
Intel·ligència artificial
Motors elèctrics d'inducció
Àrees temàtiques de la UPC::Enginyeria electrònica
title_short Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
title_full Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
title_fullStr Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
title_full_unstemmed Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
title_sort Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor
dc.creator.none.fl_str_mv Saucedo Dorantes, Juan Jose
Jaen Cuellar, Arturo Yosimar
Delgado Prieto, Miquel|||0000-0001-9282-838X
Romero Troncoso, René de Jesús
Osornio Rios, Roque A.
author Saucedo Dorantes, Juan Jose
author_facet Saucedo Dorantes, Juan Jose
Jaen Cuellar, Arturo Yosimar
Delgado Prieto, Miquel|||0000-0001-9282-838X
Romero Troncoso, René de Jesús
Osornio Rios, Roque A.
author_role author
author2 Jaen Cuellar, Arturo Yosimar
Delgado Prieto, Miquel|||0000-0001-9282-838X
Romero Troncoso, René de Jesús
Osornio Rios, Roque A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Electric motors, Induction
Condition monitoring
Multi-fault diagnosis
Feature selection
Feature reduction
Artificial intelligence
Intel·ligència artificial
Motors elèctrics d'inducció
Àrees temàtiques de la UPC::Enginyeria electrònica
topic Artificial intelligence
Electric motors, Induction
Condition monitoring
Multi-fault diagnosis
Feature selection
Feature reduction
Artificial intelligence
Intel·ligència artificial
Motors elèctrics d'inducció
Àrees temàtiques de la UPC::Enginyeria electrònica
description The development of novel condition monitoring strategies represents a critical challenge to ensure the effectiveness and reliability of complex industrial processes. Indeed, the interconnectivity of multiple variables facilitates the data exploitation under the framework of the Industry 4.0 and, subsequently, the advanced monitoring may prevent unexpected conditions. Therefore, in this work it is proposed a condition monitoring methodology based on the estimation and optimization of a high-dimensional set of hybrid features for identifying and assessing the occurrence of multiple and combined faults that appear simultaneously in an induction motor. The contribution of this work includes the high-performance characterization of the induction motor operation by means of the high-dimensional set of hybrid features which is estimated from the analysis of vibrations and stator currents through techniques from different domains. Additionally, the validation that by using artificial intelligence and machine learning-based techniques allows the implementation of stages to optimize and reduce the high-dimensional feature space, leading to the selection and retention of the most discriminative features of the considered conditions. Finally, the automated diagnostics of multiple and combined faults, performed by a Neural Network-based classifier, highlights the effectiveness of the proposed method to overcome the occurrence of multiple faults that may appear simultaneously. The proposed method is validated under a complete set of experimental data that includes the healthy condition, three single fault conditions and four combined fault conditions, where the combinations of two and three fault conditions are studied.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-06-01
2022
2022-02-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/361165
https://dx.doi.org/10.1016/j.measurement.2021.109404
url https://hdl.handle.net/2117/361165
https://dx.doi.org/10.1016/j.measurement.2021.109404
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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