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

  • Nuño Basurto
  • Álvaro HerreroEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)


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.


Anomaly detection Component-based robotic systems Preprocessing Missing values Classification Support vector machines 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica SuperiorUniversidad de BurgosBurgosSpain

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