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Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

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Abstract

The detection of anomalies (affecting hardware or software) is an open challenge for cyber-physical systems in general and robots in particular. Physical anomalies related to the hardware components of such systems have been widely researched. However, scant attention has been devoted so far to study the anomalies affecting the software components. In order to bridge this gap, the present paper proposes the application of different classifiers to a robot performance dataset for the first time. The applied supervised models are targeted at detecting synthetically-induced software anomalies, having a detrimental impact on the performance of a collaborative robot. Obtained results demonstrate that the applied Machine Learning models can successfully address the target problem, with acceptable detection rates.

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Authors and Affiliations

  1. CTC, Department of Industrial Engineering, CITIC, University of A Coruña, Avda. 19 de febrero s/n, 15405, Ferrol, A Coruña, Spain

    Héctor Quintián, Esteban Jove & José Luis Calvo-Rolle

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

    Nuño Basurto, Carlos Cambra & Álvaro Herrero

  3. Edificio Departamental, Campus Unamuno, University of Salamanca, 37007, Salamanca, Spain

    Emilio Corchado

Authors
  1. Héctor Quintián
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  2. Esteban Jove
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  3. José Luis Calvo-Rolle
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  4. Nuño Basurto
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  5. Carlos Cambra
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  6. Álvaro Herrero
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  7. Emilio Corchado
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Quintián, H. et al. (2020). Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_51

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62364-7

  • Online ISBN: 978-3-030-62365-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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