Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System

  • Esteban JoveEmail author
  • José-Luis Casteleiro-Roca
  • Héctor Quintián
  • Juan Albino Méndez-Pérez
  • José Luis Calvo-Rolle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)


One of the most important points to improve the profits in an industrial process lies on the fact of achieving a good optimisation and applying a smart maintenance plan. Under this circumstances an early anomaly plays an important role. Then, the implementation of classifiers for anomaly detection is an important challenge. As many of the anomalies that can occur in a plant have an unknown behaviour, it is necessary to generate artificial outliers to check these classifiers. This work presents different one-class intelligent techniques to perform anomaly detection in an industrial facility, used to obtain the main material for wind generator blades production. Furthermore, artificial anomaly data are generated to check the performance of each technique. The final results achieved are successful in general terms.


Anomaly detection Control system Outlier generation 


  1. 1.
    Alaiz Moretón, H., Calvo Rolle, J., García, I., Alonso Alvarez, A.: Formalization and practical implementation of a conceptual model for PID controller tuning. Asian J. Control 13(6), 773–784 (2011)CrossRefGoogle Scholar
  2. 2.
    Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  3. 3.
    Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R.F.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Log. 13(1), 37–47 (2015)CrossRefGoogle Scholar
  4. 4.
    Casale, P., Pujol, O., Radeva, P.: Approximate convex hulls family for one-class classification. In: International Workshop on Multiple Classifier Systems, pp. 106–115. Springer (2011)Google Scholar
  5. 5.
    Casteleiro-Roca, J.L., Barragán, A.J., Segura, F., Calvo-Rolle, J.L., Andújar, J.M.: Fuel cell output current prediction with a hybrid intelligent system. Complexity (2019)Google Scholar
  6. 6.
    Casteleiro-Roca, J.L., Jove, E., Sánchez-Lasheras, F., Méndez-Pérez, J.A., Calvo-Rolle, J.L., de Cos Juez, F.J.: Power cell soc modelling for intelligent virtual sensor implementation. J. Sens. (2017)Google Scholar
  7. 7.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  8. 8.
    Chen, Y., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: Proceedings of 2001 International Conference on Image Processing, vol. 1, pp. 34–37. IEEE (2001)Google Scholar
  9. 9.
    Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  10. 10.
    Fan, H., Wong, C., Yuen, M.F.: Prediction of material properties of epoxy materials using molecular dynamic simulation. In: 7th International Conference on Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, EuroSime 2006, pp. 1–4, April 2006Google Scholar
  11. 11.
    Fernández-Francos, D., Fontenla-Romero, Ó., Alonso-Betanzos, A.: One-class convex hull-based algorithm for classification in distributed environments. IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–11 (2018)Google Scholar
  12. 12.
    Garcia, R.F., Rolle, J.L.C., Castelo, J.P., Gomez, M.R.: On the monitoring task of solar thermal fluid transfer systems using NN based models and rule based techniques. Eng. Appl. Artif. Intell. 27, 129 – 136 (2014).
  13. 13.
    González, G., Angelo, C.D., Forchetti, D., Aligia, D.: Diagnóstico de fallas en el convertidor del rotor en generadores de inducción con rotor bobinado. Revista Iberoamericana de Automática e Informática industrial 15(3), 297–308 (2018). Scholar
  14. 14.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  15. 15.
    Hobday, M.: Product complexity, innovation and industrial organisation. Res. Policy 26(6), 689–710 (1998)CrossRefGoogle Scholar
  16. 16.
    Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)CrossRefGoogle Scholar
  17. 17.
    Jove, E., Aláiz-Moretón, H., Casteleiro-Roca, J.L., Corchado, E., Calvo-Rolle, J.L.: Modeling of bicomponent mixing system used in the manufacture of wind generator blades. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) Intelligent Data Engineering and Automated Learning - IDEAL 2014, pp. 275–285. Springer, Cham (2014)Google Scholar
  18. 18.
    Jove, E., Alaiz-Moretón, H., García-Rodríguez, I., Benavides-Cuellar, C., Casteleiro-Roca, J.L., Calvo-Rolle, J.L.: PID-ITS: an intelligent tutoring system for PID tuning learning process. In: International Joint Conference SOCO 2017-CISIS 2017-ICEUTE 2017, León, Spain, 6–8 September 2017, pp. 726–735. Springer (2017)Google Scholar
  19. 19.
    Jove, E., Antonio Lopez-Vazquez, J., Isabel Fernandez-Ibanez, M., Casteleiro-Roca, J.L., Luis Calvo-Rolle, J.: Hybrid intelligent system to predict the individual academic performance of engineering students. Int. J. Eng. Educ. 34(3), 895–904 (2018)Google Scholar
  20. 20.
    Jove, E., Casteleiro-Roca, J.L., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L.: A new approach for system malfunctioning over an industrial system control loop based on unsupervised techniques. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Sáez, J.A., Quintián, H., Corchado, E. (eds.) International Joint Conference SOCO’18-CISIS’18-ICEUTE’18, pp. 415–425. Springer, Cham (2018)Google Scholar
  21. 21.
    Jove, E., Gonzalez-Cava, J.M., Casteleiro-Roca, J.L., Méndez-Pérez, J.A., Antonio Reboso-Morales, J., Javier Pérez-Castelo, F., Javier de Cos Juez, F., Luis Calvo-Rolle, J.: Modelling the hypnotic patient response in general anaesthesia using intelligent models. Log. J. IGPL 27, 189–201 (2018)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Moreno-Fernandez-de Leceta, A., Lopez-Guede, J.M., Ezquerro Insagurbe, L., Ruiz de Arbulo, N., Graña, M.: A novel methodology for clinical semantic annotations assessment. Log. J. IGPL 26(6), 569–580 (2018).
  23. 23.
    Li, K.L., Huang, H.K., Tian, S.F., Xu, W.: Improving one-class SVM for anomaly detection. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3077–3081. IEEE (2003)Google Scholar
  24. 24.
    Manuel Vilar-Martinez, X., Aurelio Montero-Sousa, J., Luis Calvo-Rolle, J., Luis Casteleiro-Roca, J.: Expert system development to assist on the verification of "TACAN" system performance. Dyna 89(1), 112–121 (2014)Google Scholar
  25. 25.
    MathWorks: Autoencoder. Accessed 29 Jan 2019
  26. 26.
    MathWorks: fitcsvm. Accessed 29 Jan 2019
  27. 27.
  28. 28.
    Miljković, D.: Fault detection methods: a literature survey. In: 2011 Proceedings of the 34th International Convention on MIPRO, pp. 750–755. IEEE (2011)Google Scholar
  29. 29.
    Pei, Y., Zaïane, O.: A synthetic data generator for clustering and outlier analysis. University of Alberta, edmonton, AB, Canada, Department of Computing science (2006)Google Scholar
  30. 30.
    Quintián, H., Casteleiro-Roca, J.L., Perez-Castelo, F.J., Calvo-Rolle, J.L., Corchado, E.: Hybrid intelligent model for fault detection of a lithium iron phosphate power cell used in electric vehicles. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 751–762. Springer (2016)Google Scholar
  31. 31.
    Quintián, H., Corchado, E.: Beta scale invariant map. Eng. Appl. Artif. Intell. 59, 218–235 (2017)CrossRefGoogle Scholar
  32. 32.
    Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113, 130503 (2014). Scholar
  33. 33.
    Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)Google Scholar
  34. 34.
    Segovia, F., Górriz, J.M., Ramírez, J., Martinez-Murcia, F.J., García-Pérez, M.: Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders. Log. J. IGPL 26(6), 618–628 (2018). Scholar
  35. 35.
    Shalabi, L.A., Shaaban, Z.: Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: 2006 International Conference on Dependability of Computer Systems, pp. 207–214, May 2006Google Scholar
  36. 36.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Wang, C.K., Ting, Y., Liu, Y.H., Hariyanto, G.: A novel approach to generate artificial outliers for support vector data description. In: IEEE International Symposium on Industrial Electronics, ISIE 2009, pp. 2202–2207. IEEE (2009)Google Scholar
  38. 38.
    Wojciechowski, S.: A comparison of classification strategies in rule-based classifiers. Log. J. IGPL 26(1), 29–46 (2018). Scholar
  39. 39.
    Zeng, Z., Wang, J.: Advances in Neural Network Research and Applications, 1st edn. Springer Publishing Company, Heidelberg (2010). IncorporatedCrossRefGoogle Scholar
  40. 40.
    Zotes, F.A., Peñas, M.S.: Heuristic optimization of interplanetary trajectories in aerospace missions. Revista Iberoamericana de Automática e Informática Industrial RIAI 14(1), 1–15 (2017).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Esteban Jove
    • 1
    • 2
    Email author
  • José-Luis Casteleiro-Roca
    • 1
  • Héctor Quintián
    • 1
  • Juan Albino Méndez-Pérez
    • 2
  • José Luis Calvo-Rolle
    • 1
  1. 1.Department of Industrial EngineeringUniversity of A CoruñaFerrol, A CoruñaSpain
  2. 2.Department of Computer Science and System EngineeringUniversidad de La LagunaS/C de TenerifeSpain

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