Incident Detection in Industrial Processes Utilizing Machine Learning Techniques

  • Giorgos Tziroglou
  • Thanasis VafeiadisEmail author
  • Chrysovalantou Ziogou
  • Stelios Krinidis
  • Spyros Voutetakis
  • Dimitrios Tzovaras
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 637)


This work provides a comparative analysis of the most popular and widely used classification algorithms applied in industrial processes, in order to tackle the issue of incident detection of abnormal situations. The proposed analysis is based on actual datasets from derived by the operation of a chemical process system situated at the premises of CERTH/CPERI. The evaluation of the tested methods is based on cross-validation using a series of Monte-Carlo simulations among several free parameters of tested classifiers and finally the application of the Adaptive Boosting technique. The experimental results are highly reliable as the accuracy reaches 98% and the F-measure metric achieves a score over 97%. Therefore, the detection of potential malfunctions is achieved using the proposed machine learning techniques.


Machine learning techniques Incident detection AdaBoost 



This work has been partially supported by the European Commission through the project HORIZON 2020-INNOVATION ACTIONS (IA)-636302-SATISFACTORY.


  1. 1.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual Workshop on Computational Learning Theory, ACM, pp. 144–152 (1992)Google Scholar
  2. 2.
    Breiman, L.: Random forests – random features. Technical Report 567, Statistics Department, University of California, Berkeley,
  3. 3.
    Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory, Springer, pp. 23–37 (1995)Google Scholar
  4. 4.
    Ge, Z., Song, Z.: Semiconductor manufacturing process monitoring based on adaptive substatistical PCA. IEEE Trans. Semicond. Manuf. 23(1), 99–108 (2010)CrossRefGoogle Scholar
  5. 5.
    Ghouse, F., Natarajan, P., Subramanian, S.: Fault diagnosis of batch reactor using machine learning methods. Model. Simul. Eng. 2014 (2014), Article ID 426402Google Scholar
  6. 6.
    Gowthami, R., Vijayachitra, S.: Fault detection and diagnosis in continuous stirred tank reactor (CSTR). Int. J. Tech. Res. Appl. 3(2), 07–11 (2015)Google Scholar
  7. 7.
    Lee, S.J., Siau, K.: A review of data mining techniques. Ind. Manag. Data Syst. 101(1), 41–46 (2001)CrossRefGoogle Scholar
  8. 8.
    Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Process. 83(12), 2481–2497 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Munirathinam, S., Ramadoss, B.: Predictive models for equipment fault detection in the semiconductor manufacturing process. Int. J. Eng. Technol. 8(4), 273 (2016)CrossRefGoogle Scholar
  10. 10.
    Nath, S.V., Behara, R.S.: Customer churn analysis in the wireless industry: a data mining approach. In: Annual Meeting of the Decision Sciences Institute, pp. 505–510 (2003)Google Scholar
  11. 11.
    Papadopoulos, S., Drosou, A., Dimitriou, N., Abdelrahman, O.H., Gorbil, G., Tzovaras, D.: A BRPCA based approach for anomaly detection in mobile networks. In: 30th International Symposium on Computer and Information Sciences, vol. 363, pp. 115–124 (2015)Google Scholar
  12. 12.
    Praveenkumara, T., Saimuruganb, M., Krishnakumarc, P., Ramachandrand, K.I.: Fault diagnosis of automobile gearbox based on machine learning techniques. Procedia Eng. 97, 2092–2098 (2014)CrossRefGoogle Scholar
  13. 13.
    Russell, E.L., Chiang, L.H., Braatz, R.D.: Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometr. Intell. Lab. Syst. 51, 81–93 (2000)CrossRefGoogle Scholar
  14. 14.
    Santos, P., Villa, L.F., Reñones, A., Bustillo, A., Maudes, J.: An SVM-based solution for fault detection in wind turbines. Sensors 15, 5627–5648 (2015). doi: 10.3390/s150305627 CrossRefGoogle Scholar
  15. 15.
    Schapire, R.E., Freund, Y.: Boosting: Foundations and Algorithms. MIT Press, Cambridge (2012)zbMATHGoogle Scholar
  16. 16.
    Thiprungsri, S., Vasarhelyi, M.A.: Cluster analysis for anomaly detection in accounting data: an audit approach. Int. J. Digit. Acc. Res. 11, 69–84 (2011)Google Scholar
  17. 17.
    Vafeiadis, T., Ioannidis, D., Krinidis, S., Ziogou, C., Voutetakis, S., Tzovaras, D., Likothanassis, S.: Real-time incident detection: An approach for two interdependent time series. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 1418–1422 (2016)Google Scholar
  18. 18.
    Yuan, F., Cheu, R.L.: Incident detection using support vector machines. Transp. Res. Part C Emerg. Technol. 11(3), 309–328 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Giorgos Tziroglou
    • 1
  • Thanasis Vafeiadis
    • 2
    Email author
  • Chrysovalantou Ziogou
    • 3
  • Stelios Krinidis
    • 2
  • Spyros Voutetakis
    • 3
  • Dimitrios Tzovaras
    • 2
  1. 1.School of Electrical and Computer Engineering, Facility of EngineeringAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Information Technologies InstituteCenter for Research and Technology HellasThessalonikiGreece
  3. 3.Chemical Process and Energy Resources InstituteCenter for Research and Technology HellasThessalonikiGreece

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