Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuits

  • Christian Borgelt
  • Rudolf Kruse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3571)


We describe an algorithm to build a graphical model—more precisely: a join tree representation of a Markov network—for a steady state analog electrical circuit. This model can be used to do probabilistic diagnosis based on manufacturer supplied information about nominal values of electrical components and their tolerances as well as measurements made on the circuit. Faulty components can be identified by looking for high probabilities for values of characteristic magnitudes that deviate from the nominal values.


Graphical Model Marginal Distribution Maximal Clique Analog Circuit Conditional Probability Distribution 
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  1. 1.
    Aminian, F., Aminian, M., Collins, H.W.: Analog Fault Diagnosis of Actual Circuits Using Neural Networks. IEEE Trans. Instrumentation and Measurement 51(3), 544–550 (2002)CrossRefGoogle Scholar
  2. 2.
    Bandler, J.W., Salama, A.E.: Fault Diagnosis of Analog Circuits. Proc. IEEE, 1279–1325 (1985)Google Scholar
  3. 3.
    Borgelt, C., Kruse, R.: Graphical Models — Methods for Data Analysis and Mining. J. Wiley & Sons, Chichester (2002)zbMATHGoogle Scholar
  4. 4.
    Castillo, E., Gutierrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, New York (1997)Google Scholar
  5. 5.
    Csiszar, I.: I-Divergence Geometry of Probability Distributions and Indirect Observations. Studia Scientiarum Mathematicarum Hungarica 2, 299–318 (1975)MathSciNetGoogle Scholar
  6. 6.
    Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996)Google Scholar
  7. 7.
    Jiroušek, R., Pøeuèil, S.: On the Effective Implementation of the Iterative Proportional Fitting Procedure. Computational Statistics and Data Analysis 19, 177–189 (1995); Int. Statistical Institute, Voorburg, Netherlands (1995)zbMATHCrossRefGoogle Scholar
  8. 8.
    de Kleer, J., Williams, B.C.: Diagnosing Multiple Faults. Artificial Intelligence 32(1), 97–130 (1987)zbMATHCrossRefGoogle Scholar
  9. 9.
    Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)Google Scholar
  10. 10.
    Liu, R.-W. (ed.): Selected Papers on Analog Fault Diagnosis. IEEE Press, New York (1987)Google Scholar
  11. 11.
    Liu, R.-W.: Testing and Diagnosis of Analog Circuits and Systems. Van Nostrand Reinhold, New York (1991)Google Scholar
  12. 12.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edn. Morgan Kaufman, San Mateo (1992)Google Scholar
  13. 13.
    Spina, R., Upadhyaya, S.: Linear Circuit Fault Diagnosis Using Neuromorphic Analyzers. IEEE Trans. Circuits and Systems II 44(3), 188–196 (1997)CrossRefGoogle Scholar
  14. 14.
    Whittaker, J.: Graphical Models in Applied Multivariate Statistics. J. Wiley & Sons, Chichester (1990)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christian Borgelt
    • 1
  • Rudolf Kruse
    • 1
  1. 1.School of Computer ScienceUniversity of MagdeburgMagdeburgGermany

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