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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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 14; S. 6502
Hauptverfasser:	Karapalidou, Elisavet, Alexandris, Nikolaos, Antoniou, Efstathios, Vologiannidis, Stavros, Kalomiros, John, Varsamis, Dimitrios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 18.07.2023 MDPI
Schlagworte:	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
Online-Zugang:	Volltext
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