Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples.

Wei, Meirong; Liu, Yan; Zhang, Tao; Wang, Ze; Zhu, Jiaming

Wei, Meirong; Liu, Yan; Zhang, Tao; Wang, Ze; Zhu, Jiaming.

Afiliação

Wei M; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Liu Y; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Zhang T; Wuhan University Shenzhen Research Institute, Shenzhen 518057, China.
Wang Z; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Zhu J; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel) ; 22(1)2021 Dec 28.

Article em En | MEDLINE | ID: mdl-35009734

Assuntos

Algoritmos; Redes Neurais de Computação; Aprendizado de Máquina Supervisionado

Palavras-chave

DTC-SimCLR; data transformation; fault diagnosis with very few samples; self-supervised learning

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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