A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN. (March 2023)

• Record Type: Journal Article
• Authors: Zhang, Hengqi; Geng, Hua
• Is Part Of: Reliability engineering & system safety. Volume 231(2023)
• Publication Date: 2023-03
• Subjects: Electrical personal accidents -- Causation network -- Weighted betweenness -- Risk metric; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.109027
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: Many electrical personal accidents occurred repeatedly due to similar high-risk causes, and the lack of making targeted measures has brought challenges to the risk control. This paper proposes a methodology to identify and assess the high-risk causes of electrical personal accidents based on directed weighted complex network (CN). In this methodology, the electrical personal accident causation network (EPACN) is constructed according to accident reports, representing the complex interaction relationships between various events. Aiming at identifying the causes with high accident risks, a weighted betweenness centrality ( W B C ) is proposed. In addition, the occurrence probability of a complete accident chain is estimated based on the cascading failure theory. Then a risk metric called S F P is proposed to implement the risk assessment, which combines the consequence severity, the frequency of causes and the occurrence probability of the accident chains. The proposed method is verified on five types of electrical personal accidents, which reveals the high-risk causative events effectively. Taking targeted measures to high-risk causes can provide references for enterprises and enhance the safety of electrical operations. Highlights: A methodology to identify and assess the high-risk causes is proposed. A weighted betweenness is proposed to identify the causes with high accident risks. A risk metric is proposed considering the frequency, severity and probability.