Extreme-Low-Speed Heavy Load Bearing Fault Diagnosis by Using Improved RepVGG and Acoustic Emission Signals.

Jiang, Peng; Sun, Wenyu; Li, Wei; Wang, Hongyu; Liu, Cong

Jiang, Peng; Sun, Wenyu; Li, Wei; Wang, Hongyu; Liu, Cong.

Afiliación

Jiang P; School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Sun W; School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Li W; School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Wang H; Beijing Tongtai Hengsheng Technology Co., Ltd., Beijing 100020, China.
Liu C; School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.

Sensors (Basel) ; 23(7)2023 Mar 28.

Article en En | MEDLINE | ID: mdl-37050599

Palabras clave

EMA; MFCCs; RepVGG; deep learning; extreme-low-speed heavy load bearing; intelligent fault diagnosis; wind turbine

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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