Performance Degradation Prediction Using LSTM with Optimized Parameters.

Hu, Yawei; Wei, Ran; Yang, Yang; Li, Xuanlin; Huang, Zhifu; Liu, Yongbin; He, Changbo; Lu, Huitian

Hu, Yawei; Wei, Ran; Yang, Yang; Li, Xuanlin; Huang, Zhifu; Liu, Yongbin; He, Changbo; Lu, Huitian.

Afiliação

Hu Y; College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Wei R; Anhui NARI Jiyuan Electric Co., Ltd., Hefei 230601, China.
Yang Y; China North Vehicle Research Institute, Beijing 100071, China.
Li X; College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Huang Z; College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Liu Y; College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
He C; College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Lu H; JJL College of Engineering, South Dakota State University, Brookings, SD 57007, USA.

Sensors (Basel) ; 22(6)2022 Mar 21.

Article em En | MEDLINE | ID: mdl-35336579

Palavras-chave

IPSO; KJADE; LSTM; degradation prediction; performance degradation; rolling bearing

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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