Health Status Recognition Method for Rotating Machinery Based on Multi-Scale Hybrid Features and Improved Convolutional Neural Networks.

Cao, Xiangang; Guo, Xingyu; Duan, Yong; Zhang, Fuqiang; Fan, Hongwei; Xu, Xin

Cao, Xiangang; Guo, Xingyu; Duan, Yong; Zhang, Fuqiang; Fan, Hongwei; Xu, Xin.

Afiliação

Cao X; School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Guo X; Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi'an 710054, China.
Duan Y; School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Zhang F; Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi'an 710054, China.
Fan H; School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Xu X; Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi'an 710054, China.

Sensors (Basel) ; 23(12)2023 Jun 18.

Article em En | MEDLINE | ID: mdl-37420853

Assuntos

Cultura; Nível de Saúde; Humanos; Redes Neurais de Computação; Análise de Componente Principal; Reconhecimento Psicológico

Palavras-chave

convolutional neural networks; health status identification; multi-scale hybrid features; rotating machinery

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nível de Saúde / Cultura Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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