An accident causation model based on safety information cognition and its application. (March 2021)

• Record Type: Journal Article
• Authors: Chen, Yuanjiang; Feng, Wei; Jiang, Zhiqiang; Duan, Lingling; Cheng, Shuangyi
• Is Part Of: Reliability engineering & system safety. Volume 207(2021)
• Publication Date: 2021-03
• Subjects: system safety -- accident causation model -- safety information cognition -- human error -- human factor analysis; 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.2020.107363
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: l A new accident causation model based on safety information cognition is developed. l The root cause of accidents includes false safety information and signal noise. l A versatile, complete, and explicit method of human factor analysis is proposed. Abstract: Accident causation models can provide a framework to explain how an accident occurs from various perspectives. In the past, human error has become the main cause of accidents, which is inextricably linked with safety information cognition (SIC). In this study, an accident causation model—SIC-based Accident-causing Model (SICAM) is developed and then a new human reliability analysis methodology—SIC-based human factor analysis (SICHFA) is provided. Three steps are followed to construct the model. First, the role of SIC in the safety information flow (SIF) has been confirmed. Secondly, the mechanism of accident causation based on SIC were addressed, the effect of negative signal noise mainly involved. Thirdly, the influence objects of signal noise on the SIC process (SICP) were classified at information home, cognitive environment, and cognitive object. To demonstrate the viability of SICAM & SICHFA, the electrical fire, which occurred frequently and resulted in serious adverse social impact in recent years, was selected as the case in this study. Results showed that the proposed accident causation model and SICHFA can provide a new approach for preventing & predicting human accidents.