Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes.

Ahn, Jisu; Lee, Younjeong; Kim, Namji; Park, Chanho; Jeong, Jongpil

Ahn, Jisu; Lee, Younjeong; Kim, Namji; Park, Chanho; Jeong, Jongpil.

Afiliação

Ahn J; Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.
Lee Y; AI Research Center, Gfyhealth, 20 Pangyo-ro, Bundang-gu, Seongnam-si 13488, Gyeonggi-do, Republic of Korea.
Kim N; Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.
Park C; AI Research Center, Gfyhealth, 20 Pangyo-ro, Bundang-gu, Seongnam-si 13488, Gyeonggi-do, Republic of Korea.
Jeong J; Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.

Sensors (Basel) ; 23(17)2023 Aug 22.

Article em En | MEDLINE | ID: mdl-37687787

Palavras-chave

LSTM; anomaly detection; data distribution; federated learning; time series

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article

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