A distributed topology for identifying anomalies in an industrial environment

Autor: Francisco Zayas-Gato, Álvaro Michelena, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Paulo Novais, Juan Albino Méndez-Pérez, José Luis Calvo-Rolle
Rok vydání: 2022
Předmět:
Zdroj: Neural Computing and Applications. 34:20463-20476
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-022-07106-7
Popis: The 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.
Databáze: OpenAIRE
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