Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units.

Karapalidou, Elisavet; Alexandris, Nikolaos; Antoniou, Efstathios; Vologiannidis, Stavros; Kalomiros, John; Varsamis, Dimitrios

Karapalidou, Elisavet; Alexandris, Nikolaos; Antoniou, Efstathios; Vologiannidis, Stavros; Kalomiros, John; Varsamis, Dimitrios.

Afiliação

Karapalidou E; Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece.
Alexandris N; Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece.
Antoniou E; Department of Informatics and Electronics Engineering, International Hellenic University, 57400 Thessaloniki, Greece.
Vologiannidis S; Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece.
Kalomiros J; Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece.
Varsamis D; Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece.

Sensors (Basel) ; 23(14)2023 Jul 18.

Article em En | MEDLINE | ID: mdl-37514798

Palavras-chave

anomaly detection; autoencoder; deep learning; industrial data; long short-term memory; predictive maintenance

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

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