Dynamic Bayesian Network Factors from Possible Conflicts for Continuous System Diagnosis

@inproceedings{Alonso2011DynamicBN,
  title={Dynamic Bayesian Network Factors from Possible Conflicts for Continuous System Diagnosis},
  author={Carlos J. Alonso and Noemi Moya Alonso and Gautam Biswas},
  booktitle={CAEPIA},
  year={2011}
}
This paper introduces a factoring method for Dynamic Bayesian Networks (DBNs) based on Possible Conflicts (PCs), which aim to reduce the computational burden of Particle Filter inference. Assuming single fault hypothesis and known fault modes, the method allows performing consistency based fault detection, isolation and identification of continuous dynamic systems, with the unifying formalism of DBNs. The three tank system benchmark has been used to illustrate the approach. Two fault scenarios… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 11 references

A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking

  • M. S. Arulampalam, S. Maskell, N. Gordon, T. Clapp
  • IEEE Transactions on Signal Processing 50(2), 174…
  • 2002
Highly Influential
4 Excerpts

A comparison of two methods for fault detection: a statistical decision, and an interval-based approach

  • E. R. Gelso, G. Biswas, S. M. Castillo, J. Armengol
  • Proceeding of the 19th International Workshop on…
  • 2008
1 Excerpt

Sampling in factored dynamic systems

  • D. Koller, U. Lerner
  • Sequential Monte Carlos Methods in Practice…
  • 2001
2 Excerpts

A theory of diagnosis from first principles

  • R. Reiter
  • Artificial Intelligence 32, 57–95
  • 1987
2 Excerpts

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