Neural Network Based Industrial Processes Monitoring

  • Luis P. Sánchez-Fernández
  • Cornelio Yáñez-Márquez
  • Oleksiy Pogrebnyak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing. This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.


Order System Circular Buffer Intelligent Data Analysis Transitory Response Continuous Mathematical Model 


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  1. 1.
    Nimmo, I.: Adequately Address Abnormal Operations. Chem. Eng. Progr. 91, 36–45 (1995)Google Scholar
  2. 2.
    Juricek, B., Larimore, W., Seborg, D.: Early Detection of Alarm Situations Using Model Predictions. In: Proc. IFAC Workshop on On-Line Fault Detection, Lyon, France (1998)Google Scholar
  3. 3.
    Juricek, B., Dale, E., Seborg, D., Larimore, W.: Predictive Monitoring for Abnormal Situation Management. Journal of Process Control 11, 111–128 (2001)CrossRefGoogle Scholar
  4. 4.
    Ogata, K.: Modern Control Engineering, 4th edn. Prentice Hall, NY (2001)Google Scholar
  5. 5.
    Boyer, S.A.: SCADA: Supervisory Control and Data Acquisition, 3rd edn. Book News, Inc., Portland (2004)Google Scholar
  6. 6.
    Södertröm, T., Stoica, P.: System Identification. Prentice-Hall, Englewood Cliffs (1989)Google Scholar
  7. 7.
    Edgar, T., Himmelblau, D.: Optimization of Chemical Processes. MacGraw-Hill, NY (1988)Google Scholar
  8. 8.
    Robins, V., et al.: Topology and Intelligent Data. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 275–285. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Oppenheim, A., Schafer, R., Buck, J.: Discrete-Time Signal Processing, 2nd edn. Prentice-Hall Int. Editions, Englewood Cliffs (1999)Google Scholar
  10. 10.
    Hooke, R., Jeeves, T.: Direct Search Solution for Numerical and Statistical Problems. Journal ACM 8, 212–221 (1961)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis P. Sánchez-Fernández
    • 1
  • Cornelio Yáñez-Márquez
    • 1
  • Oleksiy Pogrebnyak
    • 1
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico

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