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Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network by Xiao, Qin (author);Li, Yapeng (author);Luo, Fan (author);Liu, Hui (author) is a Business, Management and Accounting article available to read on EtoBox.

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Unmanned aerial vehicles (UAVs) have become increasingly prevalent in human production and life, but concerns about their impact on public safety have also grown. To address these concerns, this paper presents a hybrid risk identification model that uses fault tree analysis (FTA) and Bayesian network (BN) to analyze critical risks associated with UAVs. First, by analyzing accident reports and existing literature, the fault tree structure of UAV-related public safety accidents is constructed, and initial risk factors are identified. Next, the fault tree is converted into a BN using conversion rules and the conditional probabilities are determined based on logic gates, parameter learning, and expert opinions. The BN model is then validated using actual UAV accident cases. Finally, diagnostic inference and sensitivity analysis are applied to identify key risk factors that impact public safety from UAVs. The results indicate that the primary risk factors leading to public safety incidents involving UAVs are internal system failures (battery failures, mechanical failures, and flight control system failures), pilot factors (unqualified knowledge and skills, weak safety awareness, violati

Who reads Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network?

Author

Xiao, Qin (author);Li, Yapeng (author);Luo, Fan (author);Liu, Hui (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Business, Management and Accounting (Social Sciences)