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

The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 14; s. 6502
Hlavní autori:	Karapalidou, Elisavet, Alexandris, Nikolaos, Antoniou, Efstathios, Vologiannidis, Stavros, Kalomiros, John, Varsamis, Dimitrios
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 18.07.2023 MDPI
Predmet:	anomaly detection Artificial intelligence autoencoder Datasets Deep learning Failure industrial data Industry 4.0 long short-term memory Machine learning Machinery Manufacturing Mediation Neural networks predictive maintenance Preventive maintenance Production costs Sustainable development deep learning anomaly detection autoencoder industrial data predictive maintenance long short-term memory
ISSN:	1424-8220, 1424-8220
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