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Neural Computing and Applications
Article . 2022 . Peer-reviewed
License: CC BY
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Universidade do Minho: RepositoriUM
Other literature type . 2022
License: CC BY
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A distributed topology for identifying anomalies in an industrial environment

Authors: Francisco Zayas-Gato; Álvaro Michelena; Esteban Jove; José-Luis Casteleiro-Roca; Héctor Quintián; Paulo Novais; Juan Albino Méndez-Pérez; +1 Authors

A distributed topology for identifying anomalies in an industrial environment

Abstract

AbstractThe devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed.

Countries
Spain, Portugal
Keywords

MST, PCA, Science & Technology, kNN, Ciências Naturais::Ciências da Computação e da Informação, Anomaly detection, One-class, SVDD, Control system, NCBoP

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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2
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