Abstract
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 are discussed and a comparison of the behaviors of a DBN of the complete system with the DBN factors is also included. Comparison has confirmed that DBN computation is more efficient for factors than for the complete DBN.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Dearden, R., Clancy, D.: Particle filters for real-time fault detection in planetary rovers. In: Proceeding of the 12th International Workshop on Principles of Diagnosis, pp. 1–6 (2001)
Gelso, E.R., Biswas, G., Castillo, S.M., Armengol, J.: A comparison of two methods for fault detection: a statistical decision, and an interval-based approach. In: Proceeding of the 19th International Workshop on Principles of Diagnosis, DX 2008 (2008)
de Kleer, J., Williams, B.: Diagnosing with behavioral modes. In: Eleventh International Joint Conference on Artificial Intelligence, IJCAI 1989 (1989)
Koller, D., Lerner, U.: Sampling in factored dynamic systems. In: Sequential Monte Carlos Methods in Practice. Springer, Heidelberg (2001)
Moya, N., Biswas, G., Alonso-González, C., Koutsoukos, X.: Structural observability. application to decompose a system with possible conflicts. In: Proceeding of the 21th International Workshop on Principles of Diagnosis, DX 2010 (October 2010)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, University of California, Berkeley (2002)
Narasimhan, S.: Automated diagnosis of physical systems. In: Proceedings of ICALEPCS 2007, pp. 701–705 (2007)
Pulido, B., Alonso-Gonzalez, C.: Possible conflicts: a compilation technique for consistency-based diagnosis. Part B: Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics 34(5), 2192–2206 (2004)
Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32, 57–95 (1987)
Roychoudhury, I., Biswas, G., Koutsoukos, X.: Designing distributed diagnosers for complex continuous systems. IEEE Transactions on Automation Science and Engineering (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alonso-Gonzalez, C.J., Moya, N., Biswas, G. (2011). Dynamic Bayesian Network Factors from Possible Conflicts for Continuous System Diagnosis. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)