Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings.

Saucedo-Dorantes, Juan Jose; Arellano-Espitia, Francisco; Delgado-Prieto, Miguel; Osornio-Rios, Roque Alfredo

Saucedo-Dorantes, Juan Jose; Arellano-Espitia, Francisco; Delgado-Prieto, Miguel; Osornio-Rios, Roque Alfredo.

Afiliación

Saucedo-Dorantes JJ; HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico.
Arellano-Espitia F; MCIA Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain.
Delgado-Prieto M; MCIA Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain.
Osornio-Rios RA; HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico.

Sensors (Basel) ; 21(17)2021 Aug 30.

Article en En | MEDLINE | ID: mdl-34502720

Asunto(s)

Algoritmos; Vibración; Cerámica; Análisis de Falla de Equipo; Modalidades de Fisioterapia

Palabras clave

autoencoder; bearings; deep learning; fault diagnosis; multi-domain feature extraction; vibration signal

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Vibración / Algoritmos Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: México

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