Dynamical Uncertain Causality Graph (DUCG) is an innovative model developed on the basis of dynamic causality diagram (DCD) model, which has been proved to be reliable for fault diagnosis of nuclear reactors . DUCG can represent complex uncertain causal relationship graphically, with both high efficient inference and support of incomplete expression. Therefore, DUCG is often built much larger than Bayesian Network (BN). With the expansion of graph, DUCG would also face with combined explosion. Stochastic Simulation is a common solution for combined explosions. However, it is almost impossible to use traditional sampling algorithms for DUCG as the joint probability of evidences could be less than 1e-20 . In this paper, the algorithm based on conditional stochastic simulation for the inference of DUCG was proposed. It obtains the probability of evidences by calculating the expectation of the conditional probability in sampling process instead of using the sampling frequency, which overcomes the difficulty. What's more, this algorithm uses recursive reasoning method of DUCG to calculate conditional probability distributions of node for sampling, which means this process only depends on its parent nodes' states. As a result, it features in lower time complexity. In addition, it has the potential of parallelization like other sampling algorithms. In conclusion, this algorithm is promising to provide a new solution to the inference of the DUCG in large-scale and complex state situations.


Stochastic Simulation Method for Reasoning of Dynamic Uncertain Causality Graph (DUCG)

Division / Sponsors: 
Power Division & Nuclear Engineering Division (NED)

Power Conference
Paper Proceedings: https://asmedigitalcollection.asme.org/POWER/POWER2020/volume/83747

International Conference on Nuclear Engineering
Paper Proceedings: https://asmedigitalcollection.asme.org/ICONE/ICONE2020/volume/83761

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