Towards system-theoretic risk assessment for future ships: A framework for selecting Risk Control Options

被引:45
作者
Chaal, Meriam [1 ]
Bahootoroody, Ahmad [1 ]
Basnet, Sunil [1 ]
Banda, Osiris A. Valdez [1 ]
Goerlandt, Floris [2 ]
机构
[1] Aalto Univ, Dept Mech Engn, Marine & Arctic Technol Grp, Espoo 11000, Finland
[2] Dalhousie Univ, Dept Ind Engn, Halifax, NS B3H 4R2, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Risk control options; Bayesian network; STPA; Autonomous ships; Marine Formal Safety Assessment; Risk -based design; OIL-SPILL; MODEL; MAINTENANCE; UNCERTAINTY; STAMP;
D O I
10.1016/j.oceaneng.2022.111797
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
While the concept of smart shipping is expected to shape the future of the maritime industry, its safety is still a major concern. New risks might emerge when shifting from human controllers onboard, to autonomous software controllers and remote human controllers. The uncertainties associated with the emerging risks require an efficient decision-making methodology to ensure ship safety. This paper proposes a framework for selecting Risk Control Options (RCOs) of ships with higher degrees of autonomy in the context of marine risk assessment and Formal Safety Assessment (FSA). The framework uses the System Theoretic Process Analysis (STPA) for the hazard analysis and the identification of RCOs, while Bayesian Network is employed in the framework for estimating the system risk. Integrating STPA and BN offers the possibility to cover most of the steps of both risk assessment and FSA and permits the prioritization of the identified RCOs. The proposed method is applied to a concept of an autonomous seawater cooling system (SWC) as an illustrative case study. The results indicate that the RCOs including sensors health monitoring and software testing should be prioritized to reduce the risk. This is unveiled by the STPA analysis which shows the risk contribution of the associated causal scenarios.
引用
收藏
页数:15
相关论文
共 69 条
[1]   A multinomial process tree for reliability assessment of machinery in autonomous ships [J].
Abaei, Mohammad Mahdi ;
Hekkenberg, Robert ;
BahooToroody, Ahmad .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 210
[2]  
American Bureau of Shipping (ABS), 2020, GUID NOT RISK ASS AP
[3]  
[Anonymous], 2009, BAYESIAN NETWORKS DE
[4]  
[Anonymous], 2002, Offshore reliability data handbook
[5]   Analysis of the influence of human errors on the occurrence of coastal ship accidents in different wave conditions using Bayesian Belief Networks [J].
Antao, Pedro ;
Guedes Soares, C. .
ACCIDENT ANALYSIS AND PREVENTION, 2019, 133
[6]  
Antoine B., 2013, THESIS
[7]   Risk assessment and risk management: Review of recent advances on their foundation [J].
Aven, Terje .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2016, 253 (01) :1-13
[8]   Practical implications of the new risk perspectives [J].
Aven, Terje .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2013, 115 :136-145
[9]   Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation [J].
BahooToroody, Ahmad ;
De Carlo, Filippo ;
Paltrinieri, Nicola ;
Tucci, Mario ;
Van Gelder, P. H. A. J. M. .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2020, 201
[10]   Multi-level optimization of maintenance plan for natural gas system exposed to deterioration process [J].
BahooToroody, Ahmad ;
Abaei, Mohammad Mandi ;
Arzaghi, Ehsan ;
BahooToroody, Farshad ;
De Carlo, Filippo ;
Abbassi, Rouzbeh .
JOURNAL OF HAZARDOUS MATERIALS, 2019, 362 :412-423