Virtual-reality-generated-data-driven Bayesian networks for risk analysis

Risk analysis is crucial to the risk control of major accidents. Therefore, the risk analysis of complex systems has attracted increasing attention from academia and industry. Data-driven Bayesian network (BN) models have proved to be useful for risk analysis in complex systems. Nevertheless, insufficient data remains a challenge for risk analysis. In this paper, we propose a method of virtual reality (VR)-generated data aiming to provide a solution to generate data for risk analysis. To demonstrate the feasibility of VR-generated data applied to data-driven risk analysis, we proposed the following methodology on the example of an emergency response system for deepwater blowout (i.e., a spilled oil collection system). Firstly, a VR model of the spilled oil collection system is established. Secondly, required data is generated from the VR system for the risk analysis of emergency operations. Eventually, the data-driven BN for risk analysis is constructed based on VR-generated data. The results show that VR-generated data can support risk analysis in the presence of limited data.