Abstract
Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for diagnoses of complex systems. It performs well for industry system such as nuclear power plants, chemical system and spacecrafts. However, the variable state combination explosion in some cases is still a problem that may result in inefficiency or even disability in DUCG inference. In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation. Monte Carlo sampling is a typical algorithm to solve this problem. However, we are facing the case that the occurrence rate of the case is very small, e.g. $10^{-20}$, which means a huge number of samplings are needed. This paper proposes a new scheme based on conditional stochastic simulation which obtains the final result from the expectation of the conditional probability in sampling loops instead of counting the sampling frequency, and thus overcomes the problem. As a result, the proposed algorithm requires much less time than the DUCG recursive inference algorithm presented earlier. Moreover, a simple analysis of convergence rate based on a designed example is given to show the advantage of the proposed method. % In addition, supports for logic gate, logic cycles, and parallelization, which exist in DUCG, are also addressed in this paper. The new algorithm reduces the time consumption a lot and performs 3 times faster than old one with 2.7% error ratio in a practical graph for Viral Hepatitis B.
Highlights
Computer-aided diagnosis for finding root causes of system abnormalities are desired for large and complex systems, e.g. fault diagnoses of nuclear power plants and clinical diagnoses
Dynamic Uncertain Causality Graph (DUCG) is developed from dynamic causality diagram (DCD)[7], formally presented in [1], and evolves in recent years
In [8], a new algorithm called cubic-DUCG was introduced for temporal inference of DUCG
Summary
Computer-aided diagnosis for finding root causes of system abnormalities are desired for large and complex systems, e.g. fault diagnoses of nuclear power plants and clinical diagnoses. As one of the models, DUCG (dynamic uncertain causality graph) is recently developed [1]–[6] It is based on domain knowledge with uncertainty, and has high diagnosis precision, strong interpretability and no generalization problem that machine learning approaches usually have. A new sampling-based algorithm is proposed to boost the inference on these given models.
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