Learning Temporal Bayesian Networks for Power Plant Diagnosis

  • Pablo Hernandez-Leal
  • L. Enrique Sucar
  • Jesus A. Gonzalez
  • Eduardo F. Morales
  • Pablo H. Ibarguengoytia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6703)

Abstract

Diagnosis in industrial domains is a complex problem because it includes uncertainty management and temporal reasoning. Dynamic Bayesian Networks (DBN) can deal with this type of problem, however they usually lead to complex models. Temporal Nodes Bayesian Networks (TNBNs) are an alternative to DBNs for temporal reasoning that result in much simpler and efficient models in certain domains. However, methods for learning this type of models from data have not been developed. In this paper we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method has three phases: (i) obtain an initial interval approximation, (ii) learn the network structure based on the intervals, and (iii) refine the intervals for each temporal node. The number of possible sets of intervals is obtained for each temporal node based on a clustering algorithm and the set of intervals that maximizes the prediction accuracy is selected. We applied this method to learn a TNBN for diagnosis and prediction in a combined cycle power plant. The proposed algorithm obtains a simple model with high predictive accuracy.

Keywords

Bayesian Network Gaussian Mixture Model Steam Turbine Steam Generator Dynamic Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arroyo-Figueroa, G., Sucar, L.E.: A temporal Bayesian network for diagnosis and prediction. In: Proceedings of the 15th UAI Conference, pp. 13–22 (1999)Google Scholar
  2. 2.
    Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine learning 9(4), 309–347 (1992)MATHGoogle Scholar
  3. 3.
    Dagum, P., Galper, A., Horvitz, E.: Dynamic network models for forecasting. In: Proc. of the 8th Workshop UAI, pp. 41–48 (1992)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  5. 5.
    Galán, S.F., Arroyo-Figueroa, G., Díez, F.J., Sucar, L.E.: Comparison of two types of Event Bayesian Networks: A case study. Applied Artif. Intel. 21(3), 185 (2007)CrossRefGoogle Scholar
  6. 6.
    Knox, W.B., Mengshoel, O.: Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study. In: SAS 2009, p. 67 (2009)Google Scholar
  7. 7.
    Liu, W., Song, N., Yao, H.: Temporal Functional Dependencies and Temporal Nodes Bayesian Networks. The Computer Journal 48(1), 30–41 (2005)CrossRefGoogle Scholar
  8. 8.
    Neapolitan, R.E.: Learning Bayesian Networks. Pearson Prentice Hall, London (2004)Google Scholar
  9. 9.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pablo Hernandez-Leal
    • 1
  • L. Enrique Sucar
    • 1
  • Jesus A. Gonzalez
    • 1
  • Eduardo F. Morales
    • 1
  • Pablo H. Ibarguengoytia
    • 2
  1. 1.Optics and Electronics TonantzintlaNational Institute of AstrophysicsPueblaMexico
  2. 2.Electrical Research Institute CuernavacaMorelosMexico

Personalised recommendations