贝叶斯网络
风险分析(工程)
过程(计算)
工程类
稳健性(进化)
事故(哲学)
工艺安全
计算机科学
可靠性工程
在制品
机器学习
运营管理
医学
基因
认识论
操作系统
生物化学
哲学
化学
作者
Esmaeil Zarei,Mohammad Yazdi,Rouzbeh Abbassi,Faisal Khan
标识
DOI:10.1016/j.jlp.2018.11.015
摘要
Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.
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