A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion.

Peng, Cheng; Chen, Yufeng; Chen, Qing; Tang, Zhaohui; Li, Lingling; Gui, Weihua

Peng, Cheng; Chen, Yufeng; Chen, Qing; Tang, Zhaohui; Li, Lingling; Gui, Weihua.

Afiliación

Peng C; School of Computer, Hunan University of Technology, Zhuzhou 412007, China.
Chen Y; School of Automation, Central South University, Changsha 410083, China.
Chen Q; School of Computer, Hunan University of Technology, Zhuzhou 412007, China.
Tang Z; School of Computer, Hunan University of Technology, Zhuzhou 412007, China.
Li L; School of Automation, Central South University, Changsha 410083, China.
Gui W; School of Computer, Hunan University of Technology, Zhuzhou 412007, China.

Sensors (Basel) ; 21(2)2021 Jan 08.

Article en En | MEDLINE | ID: mdl-33435633

Palabras clave

Long short-term memory (LSTM); Temporal and spatial features; one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN); remaining useful life (RUL); turbofan engine

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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