A newly developed approach named as dynamic uncertain causality graph (DUCG) is initially presented in a previous paper, in which only the inference algorithm in terms of individual events and probabilities is addressed.  In this paper, we first explain the statistic basis of DUCG.  It is revealed that the representation of DUCG can be incomplete and the exact probabilistic inference may still be made.  A real application of DUCG for fault diagnoses of a generator system of a nuclear power plant is demonstrated, which involves > 600 variables. 
DUCG
Many approaches have been developed such as Bayesian network. 
Bayesian network
A newly developed approach named as dynamic uncertain causality graph (DUCG) is initially presented in a previous paper, in which only the inference algorithm in terms of individual events and probabilities is addressed.  In this paper, we first explain the statistic basis of DUCG.  It is revealed that the representation of DUCG can be incomplete and the exact probabilistic inference may still be made.  A real application of DUCG for fault diagnoses of a generator system of a nuclear power plant is demonstrated, which involves > 600 variables. 
DUCG