Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador

  1. Jove, E. 1
  2. Casteleiro-Roca, J. 1
  3. Quintián, H. 1
  4. Méndez-Pérez, J. A. 2
  5. Calvo-Rolle, J. L. 1
  1. 1 Univesidade da Coruña,
  2. 2 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2020

Volumen: 17

Número: 1

Páginas: 84-93

Tipo: Artículo

DOI: 10.4995/RIAI.2019.11055 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

Technological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmental impact reduction. In a context where the reduction of pollution gasses emission is promoted, the use of alternative energies, specially the wind energy, plays a key role. The wind generator blades are usually manufactured from bicomponent material, obtained from the mixture of two dierent primary components. The present research assesses dierent one-class intelligent techniques to perform anomaly detection on a bicomponent mixing system used on the wind generator manufacturing. To perform the anomaly detection, the intelligent models were obtained from real dataset recorded during the right operation of a bicomponent mixing plant. The classifiers for each technique were validated using artificial outliers, achieving very good results.

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Independientemente de la técnica aplicada, ésta ha sido va-lidada utilizando una validación cruzada k − fold con un valor k = 10. A su vez, se ha repetido dos veces esta validación, con el objetivo de evaluar, para una configuración determinada, la desviación entre los resultados de cada una de las iteraciones (Krstajic et al., 2014). El comportamiento de los clasificado-res es evaluado a través del parámetro Área Bajo la Curva ( %) (AUC por sus siglas en inglés). Este parámetro, que compara el ratio de verdaderos positivos y falsos positivos, ha demostrado ser un indicador representativo en este tipo de tareas (Bradley, 1997). Además, se evalúa la Desviación Típica (DT) del AUC obtenido en las distintas repeticiones, asícomo el tiempo de en-trenamiento te y el tiempo de cómputo tcomp, es decir, el tiempo que necesita el clasificador para detectar la anomalía.

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