Safety assessment of marine high-end equipment based on evidential reasoning approach under fuzzy uncertainty

被引:0
作者
Zhou M. [1 ,2 ]
Xiong X.-D. [1 ,2 ]
Pei F. [1 ,2 ]
机构
[1] School of Management, Hefei University of Technology, Anhui, Hefei
[2] Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, Hefei
基金
中国国家自然科学基金;
关键词
evidential reasoning; group analytical hierarchical process; marine high-end equipment; Safety assessment; uncertainty;
D O I
10.3233/JIFS-237750
中图分类号
X9 [安全科学];
学科分类号
0837 ;
摘要
Marine high-end equipment reflects a country's comprehensive national strength. The safety assessment of it is very important to avoid accident either from human or facility factors. Attribute structure and assessment approach are two key points in the safety assessment of marine high-end equipment. In this paper, we construct a hierarchical attribute structure based on literature review and text mining of reports and news. The hierarchical attribute structure includes human, equipment, environment and management level. The correlations among these attributes are analyzed. The assessment standards of attributes are described in details. Different evaluation grades associated with attributes are transformed to a unified one by the given rules. As for the assessment approach, the evidential reasoning approach is applied for uncertain information fusion. Group analytical hierarchical process is used to generate attribute weights from a group of experts, where process aggregation method and result aggregation method are combined in a comprehensive way. The importance of expert is computed by the uncertainty measure of expert's subjective judgment. A drilling platform is finally assessed by the proposed attribute structure and assessment approach to illustrate the effectiveness of the assessment framework. © 2024 - IOS Press. All rights reserved.
引用
收藏
页码:7495 / 7520
页数:25
相关论文
共 72 条
[31]  
Skogdalen J.E., Vinnem J.E., Quantitative risk analysis of oil and gas drilling, using Deepwater Horizon as case study, Reliability Engineering and System Safety, 100, pp. 58-66, (2012)
[32]  
Qiao W.L., Liu Y., Ma X.X., Liu Y., Human factors analysis for maritime accidents based on a dynamic fuzzy Bayesian network, Risk Analysis, 40, 2020, pp. 957-980
[33]  
Huang W.C., Zhang Y., Et al., Historical data-driven risk assessment of railway dangerous goods transportation system: comparisons between entropy weight method and scatter degree method, Reliability Engineering and System Safety, 205, 2020
[34]  
Cai B.P., Liu Y.H., Et al., Application of Bayesian networks in quantitative risk assessment of Subsea Blowout Preventer operations, Risk Analysis, 33, pp. 1293-1311, (2013)
[35]  
Fang H., Xue H.X., Tang W.Y., A new approach for quantitative risk assessment of gas explosions on FPSO, Ocean Engineering, 26, 2022
[36]  
Muehlenbachs L., Cohen M.A., Gerarden T., The impact of water depth on safety and environmental performance in offshore oil and gas production, Energy Policy, 55, pp. 699-705, (2013)
[37]  
Yu Q., Teixeira A.P., Liu K., Soares C.G., Framework and application of multi-criteria ship collision risk assessment, Ocean Engineering, 250, 2022
[38]  
Slatnick S., Angevine D., Et al., Bow-ties use for high-consequence marine risks of offshore structures, Process Safety and Environmental Protection, 165, 2022, pp. 396-407
[39]  
Wang Y.F., Qin T., Li B., Sun X.F., Li Y.L., Fire probability prediction of offshore platform based on Dynamic Bayesian Network, Ocean Engineering, 145, pp. 112-123, (2017)
[40]  
Huang Y.M., Ma G.W., Li J.D., Multi-level explosion risk analysis (MLERA) for accidental gas explosion events in super-large FLNG facilities, Journal of Loss Prevention in The Process Industries, 45, pp. 242-254, (2017)