Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model.

Zhou, Jianzhong; Shan, Yahui; Liu, Jie; Xu, Yanhe; Zheng, Yang

Zhou, Jianzhong; Shan, Yahui; Liu, Jie; Xu, Yanhe; Zheng, Yang.

Afiliación

Zhou J; School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Shan Y; School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Liu J; School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Xu Y; School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Zheng Y; School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.

Sensors (Basel) ; 20(15)2020 Jul 31.

Article en En | MEDLINE | ID: mdl-32751872

Palabras clave

Gaussian process regression; convolutional neural network; degradation tendency prediction; integrated degradation index; long and short memory neural network; pumped storage unit

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google