Abstract
Bayesian networks (BNs) play an important role in performing uncertainty analysis. BNs, as a sort of directed acyclic graph with probabilities, can establish causality and clarify complex uncertain relationships to benefit risk analyze. A large number of accurate data must be obtained for precisely reasoning, but it is often difficult in human reliability analysis (HRA). Inadequate data on space launch sites make it necessary to utilize different types of data in engineering. This paper studies the uncertainty in BNs and classifies the using data. Besides, the concept of Extended BNs containing the most likely probabilities and probability boundaries is proposed to address the hybrid data problem in BNs. Accordingly, the mathematical model and usage of the Extended BNs are also developed to fuse different types of data in HRA. To verify the rationality and accuracy of this method, the Extended BN with hybrid data is applied to HRA for fueling task in space launch sites. Finally, the case study shows the validity of the uncertainty expression in Extended BNs, and the Extended BNs perform well in risk prediction and risk avoidance.
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