Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform.

Li, Guoqiang; Deng, Chao; Wu, Jun; Xu, Xuebing; Shao, Xinyu; Wang, Yuanhang

Li, Guoqiang; Deng, Chao; Wu, Jun; Xu, Xuebing; Shao, Xinyu; Wang, Yuanhang.

Afiliação

Li G; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. lgq1211@tom.com.
Deng C; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. dengchao@hust.edu.cn.
Wu J; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. wuj@hust.edu.cn.
Xu X; School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. m13627290949@163.com.
Shao X; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. shaoxy@hust.edu.cn.
Wang Y; China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 510610, China. wangyuanhang@ceprei.com.

Sensors (Basel) ; 19(12)2019 Jun 19.

Article em En | MEDLINE | ID: mdl-31248106

Palavras-chave

S-transform; bearing; convolution neural networks; fault diagnosis; sensor data

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Guideline Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

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