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

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety Pub Date : 2025-03-19 DOI:10.1016/j.ress.2025.111053

Huixing Meng , Shijun Zhao , Wenjuan Song , Mengqian Hu

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引用次数: 0

Abstract

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.

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来源期刊

Reliability Engineering & System Safety 管理科学-工程：工业

CiteScore

15.20

自引率

39.50%

发文量

621

审稿时长

67 days

期刊介绍： Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.