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.
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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
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DOI: https://doi.org/10.1007/978-3-642-25274-7_23
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