Advertisement

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)

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.

Keywords

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

References

  1. 1.
    Banerjee, T.P., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Inf. Sci. 217, 96–107 (2012)CrossRefGoogle Scholar
  2. 2.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144–152. ACM, New York (1992)Google Scholar
  3. 3.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)CrossRefGoogle Scholar
  4. 4.
    Corchado, E., Herrero, Á., Sáiz, J.M.: Testing cab-ids through mutations: on the identification of network scans. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, pp. 433–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  6. 6.
    Graña, M., Alonso, M., Izaguirre, A.: A panoramic survey on grasping research trends and topics. Cybern. Syst. 50, 40–57 (2019)CrossRefGoogle Scholar
  7. 7.
    Herrero, Á., Jiménez, A.: Improving the management of industrial and environmental enterprises by means of soft computing. Cybern. Syst. 50(1), 1–2 (2019)CrossRefGoogle Scholar
  8. 8.
    IFR: International Federation of Robotics. https://ifr.org/ifr-press-releases
  9. 9.
    Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27, 504–518 (2015)CrossRefGoogle Scholar
  10. 10.
    Pérez, H., Alfonso-Cendón, J., Fernández-Robles, L., Sánchez-Gonzaález, L., Castejón-Limas, M., Corchado, E., Quintian, H.: Use of classifiers and recursive feature elimination to assess boar sperm viability. Log. J. IGPL 26(6), 629–637 (2018)MathSciNetGoogle Scholar
  11. 11.
    Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdisc. Rev. Comput. Stat. 7(3), 223–247 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sedano, J., González, S., Herrero, Á., Baruque, B., Corchado, E.: Mutating network scans for the assessment of supervised classifier ensembles. Log. J. IGPL 21(4), 630–647 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Shon, T., Kim, Y., Lee, C., Moon, J.: A machine learning framework for network anomaly detection using SVM and GA. In: Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop, pp. 176–183, June 2005Google Scholar
  14. 14.
    Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the internet of things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 697–701, December 2014Google Scholar
  15. 15.
    Wienke, J., zu Borgsen, S.M., Wrede, S.: A data set for fault detection research on component-based robotic systems. In: Alboul, L., Damian, D., Aitken, J.M. (eds.) Towards Autonomous Robotic Systems, pp. 339–350. Springer, Cham (2016)Google Scholar
  16. 16.
    Wienke, J., Wrede, S.: A Fault Detection Data Set for Performance Bugs in Component-Based Robotic Systems (2016)Google Scholar
  17. 17.
    Wienke, J., Wrede, S.: Autonomous fault detection for performance bugs in component-based robotic systems. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3291–3297. IEEE (2016)Google Scholar
  18. 18.
    Wienke, J., Wrede, S.: Continuous regression testing for component resource utilization. In: IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pp. 273–280. IEEE (2016)Google Scholar
  19. 19.
    Zidi, S., Moulahi, T., Alaya, B.: Fault detection in wireless sensor networks through SVM classifier. IEEE Sens. J. 18(1), 340–347 (2018)CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations