Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

被引:9
作者
Wu, Yaju [1 ,2 ,3 ,4 ]
Xu, Kaili [1 ,3 ]
Wang, Ruojun [1 ,3 ]
Xu, Xiaohu [1 ,3 ]
机构
[1] Northeastern Univ, Sch Resources & Civil Engn, Shenyang, Peoples R China
[2] Shenyang Aerosp Univ, Sch Safety Engn, Shenyang, Peoples R China
[3] 3-11 Wenhua Rd, Shenyang, Peoples R China
[4] 37 Daoyi St, Shenyang, Peoples R China
来源
PLOS ONE | 2021年 / 16卷 / 08期
关键词
QUANTIFICATION; MODEL;
D O I
10.1371/journal.pone.0254861
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.
引用
收藏
页数:16
相关论文
共 33 条
[11]   The main challenges of safety science [J].
Ge, Ji ;
Xu, Kaili ;
Zheng, Xin ;
Yao, Xiwen ;
Xu, Qingwei ;
Zhang, Bohan .
SAFETY SCIENCE, 2019, 118 :119-125
[12]   Human reliability assessment for complex physical operations in harsh operating conditions [J].
Golestani, Nima ;
Abbassi, Rouzbeh ;
Garaniya, Vikram ;
Asadnia, Mohsen ;
Khan, Faisal .
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2020, 140 :1-13
[13]   A simplified CREAM prospective quantification process and its application [J].
He, Xuhong ;
Wang, Yao ;
Shen, Zupei ;
Huang, Xiangrui .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2008, 93 (02) :298-306
[14]  
Hollnagel E., 1998, Cognitive Reliability and Error Analysis Method, DOI DOI 10.1016/B978-008042848-2/50001-4
[15]  
Hou Q., 2017, CHINA IND SAFETY ENV, V43, P63
[16]   A probabilistic approach for determining the control mode in CREAM [J].
Kim, MC ;
Seong, PH ;
Hollnagel, E .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2006, 91 (02) :191-199
[17]  
Pearl J., 1988, PROBABILISTIC REASON
[18]   Human reliability analysis of the Tokai-Mura accident through a THERP-CREAM and expert opinion auditing approach [J].
Ribeiro, A. C. ;
Sousa, A. L. ;
Duarte, J. P. ;
Frutuoso e Melo, P. F. .
SAFETY SCIENCE, 2016, 87 :269-279
[19]   A survey of defuzzification strategies [J].
Roychowdhury, S ;
Pedrycz, W .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2001, 16 (06) :679-695
[20]   Technical, Temporal, and Spatial Research Challenges and Opportunities in Blockchain-Based Healthcare: A Systematic Literature Review [J].
Soltanisehat, Leili ;
Alizadeh, Reza ;
Hao, Haijing ;
Choo, Kim-Kwang Raymond .
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 2023, 70 (01) :353-368