Anomaly Detection Over an Ultrasonic Sensor in an Industrial Plant

• Esteban Jove ^{[1] [2]} ; José-Luis Casteleiro-Roca ^[1] ; Jose Manuel González-Cava ^[2] ; Héctor Quintián ^[1] ; Héctor Alaiz-Moretón ^[3] ; Bruno Baruque ^[4] ; Méndez-Pérez, Juan Albino ^[2] ; José Luis Calvo-Rolle ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
  2. [2] Universidad de La Laguna

     Universidad de La Laguna

     San Cristóbal de La Laguna, España

     
  3. [3] Universidad de León

     Universidad de León

     León, España

     
  4. [4] Universidad de Burgos

     Universidad de Burgos

     Burgos, España

     

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 492-503
• Idioma: inglés
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  • The significant industrial developments in terms of digitalization and optimization, have focused the attention on anomaly detection techniques. This work presents a detailed study about the performance of different one-class intelligent techniques, used for detecting anomalies in the performance of an ultrasonic sensor. The initial dataset is obtained from a control level plant, and different percentage variations in the sensor measurements are generated. For each variation, the performance of three one-class classifiers are assessed, obtaining very good results.