Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards...

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Autores: Saucedo Dorantes, Juan Jose, Arellano Espitia, Francisco|||0000-0001-5841-0561, Delgado Prieto, Miquel|||0000-0001-9282-838X, 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:2117/358143
Acceso en línea:https://hdl.handle.net/2117/358143
https://dx.doi.org/10.3390/s21175832
Access Level:acceso abierto
Palabra clave:Fault location (Engineering)
Bearings (Machinery)
Fault diagnosis
Bearings
Deep learning
Autoencoder
Vibration signal
Multi-domain feature extraction
Avaries--Localització
Coixinets (Maquinària)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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oai_identifier_str oai:upcommons.upc.edu:2117/358143
network_acronym_str ES
network_name_str España
repository_id_str
spelling Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearingsSaucedo Dorantes, Juan JoseArellano Espitia, Francisco|||0000-0001-5841-0561Delgado Prieto, Miquel|||0000-0001-9282-838XOsornio Rios, Roque A.Fault location (Engineering)Bearings (Machinery)Fault diagnosisBearingsDeep learningAutoencoderVibration signalMulti-domain feature extractionAvaries--LocalitzacióCoixinets (Maquinària)Àrees temàtiques de la UPC::Informàtica::Automàtica i controlScientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20212021-08-3020212021-12-10journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/358143https://dx.doi.org/10.3390/s21175832reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3581432026-05-27T15:37:01Z
dc.title.none.fl_str_mv Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
title Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
spellingShingle Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
Saucedo Dorantes, Juan Jose
Fault location (Engineering)
Bearings (Machinery)
Fault diagnosis
Bearings
Deep learning
Autoencoder
Vibration signal
Multi-domain feature extraction
Avaries--Localització
Coixinets (Maquinària)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
title_full Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
title_fullStr Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
title_full_unstemmed Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
title_sort Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings
dc.creator.none.fl_str_mv Saucedo Dorantes, Juan Jose
Arellano Espitia, Francisco|||0000-0001-5841-0561
Delgado Prieto, Miquel|||0000-0001-9282-838X
Osornio Rios, Roque A.
author Saucedo Dorantes, Juan Jose
author_facet Saucedo Dorantes, Juan Jose
Arellano Espitia, Francisco|||0000-0001-5841-0561
Delgado Prieto, Miquel|||0000-0001-9282-838X
Osornio Rios, Roque A.
author_role author
author2 Arellano Espitia, Francisco|||0000-0001-5841-0561
Delgado Prieto, Miquel|||0000-0001-9282-838X
Osornio Rios, Roque A.
author2_role author
author
author
dc.subject.none.fl_str_mv Fault location (Engineering)
Bearings (Machinery)
Fault diagnosis
Bearings
Deep learning
Autoencoder
Vibration signal
Multi-domain feature extraction
Avaries--Localització
Coixinets (Maquinària)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Fault location (Engineering)
Bearings (Machinery)
Fault diagnosis
Bearings
Deep learning
Autoencoder
Vibration signal
Multi-domain feature extraction
Avaries--Localització
Coixinets (Maquinària)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-08-30
2021
2021-12-10
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://hdl.handle.net/2117/358143
https://dx.doi.org/10.3390/s21175832
url https://hdl.handle.net/2117/358143
https://dx.doi.org/10.3390/s21175832
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
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
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
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
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 15,301603

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