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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2019: 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) pp 241–250Cite as

Data Selection to Improve Anomaly Detection in a Component-Based Robot

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 950))

Abstract

The rise in complexity of robotic systems usually leads to an increase in failures of such systems. To improve the maintenance of this type of systems and thus reducing economic costs and downtime, present paper addresses anomaly detection in a component-based robot. To do so, the problem of anomaly detection is modelled as a classification problem, being Support Vector Machine (SVM) the selected classifier. It is applied to a publicly-available and recent dataset containing useful information about the performance of the software system in a component-based robot when certain anomalies are induced. Different preprocessing strategies and data sources are compared to get the best scores for some classification metrics through cross-validation.

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Author information

Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Nuño Basurto & Álvaro Herrero

Authors
  1. Nuño Basurto
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  2. Álvaro Herrero
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Corresponding author

Correspondence to Álvaro Herrero .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Basurto, N., Herrero, Á. (2020). Data Selection to Improve Anomaly Detection in a Component-Based Robot. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_23

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