A Bayesian approach to construct bow tie diagrams for risk evaluation

Abstract Bow tie diagrams have become a popular method for risk analysis and safety management. This tool describes the whole scenario of a given risk graphically, and proposes preventive and protective barriers to reduce, respectively, its occurrence and its severity. The weakness of bow tie diagrams is that they are restricted to a graphical representation of different scenarios exclusively designed by experts that ignore the dynamic aspect of real systems. Thus, constructing bow tie diagrams in an automatic and dynamic way remains a real challenge. This paper proposes a new Bayesian approach to construct bow tie diagrams from real data and improve them by adding a new numerical that enables us to implement the appropriate preventive and protective barriers in a dynamic manner.

[1]  Roger M. Cooke,et al.  Expert judgment study for placement ladder bowtie , 2008 .

[2]  R. Sadiq,et al.  Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach , 2013 .

[3]  Faisal Khan,et al.  Use Maximum-Credible Accident Scenarios for Realistic and Reliable Risk Assessment , 2001 .

[4]  J. E. Cockshott Probability Bow-Ties: A Transparent Risk Management Tool , 2005 .

[5]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[6]  Brian Veitch,et al.  Methodology for computer aided fuzzy fault tree analysis , 2009 .

[7]  Snorre Sklet,et al.  Comparison of some selected methods for accident investigation. , 2004, Journal of hazardous materials.

[8]  Nahla Ben Amor,et al.  A dynamic barriers implementation in Bayesian-based bow tie diagrams for risk analysis , 2010, ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010.

[9]  Nijs Jan Duijm,et al.  Safety-barrier diagrams as a safety management tool , 2009, Reliab. Eng. Syst. Saf..

[10]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[11]  Adam S. Markowski,et al.  Fuzzy logic for process safety analysis , 2009 .

[12]  Richard Gowland,et al.  The accidental risk assessment methodology for industries (ARAMIS)/layer of protection analysis (LOPA) methodology: a step forward towards convergent practices in risk assessment? , 2006, Journal of hazardous materials.

[13]  Cécile Fiévez,et al.  ARAMIS project: a more explicit demonstration of risk control through the use of bow-tie diagrams and the evaluation of safety barrier performance. , 2006, Journal of hazardous materials.

[14]  Ernest J. Henley,et al.  Reliability engineering and risk assessment , 1981 .

[15]  Hiroshi Koseki,et al.  Thermal characteristics and their relevance to spontaneous ignition of refuse plastics/paper fuel , 2009 .

[16]  Ross D. Shachter,et al.  Fusion and Propagation with Multiple Observations in Belief Networks , 1991, Artif. Intell..

[17]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[18]  Christian Delvosalle,et al.  ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries. , 2006, Journal of hazardous materials.

[19]  Brian Veitch,et al.  Handling data uncertainties in event tree analysis , 2009 .

[20]  Celeste Jacinto,et al.  A semi-quantitative assessment of occupational risks using bow-tie representation , 2010 .

[21]  Michalis Christou,et al.  Identification of reference accident scenarios in SEVESO establishments , 2005, Reliab. Eng. Syst. Saf..

[22]  Adam S. Markowski,et al.  “Bow-tie” model in layer of protection analysis , 2011 .

[23]  M. Konstantinidou,et al.  Comparison of techniques for accident scenario analysis in hazardous systems , 2004 .

[24]  Nahla Ben Amor,et al.  A New Approach to Construct Optimal Bow Tie Diagrams for Risk Analysis , 2010, IEA/AIE.

[25]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[26]  Judea Pearl,et al.  The recovery of causal poly-trees from statistical data , 1987, Int. J. Approx. Reason..