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Journal of Offshore Mechanics and Arctic Engineering | Volume 131 | Issue 4 | Ocean Engineering
Ocean Engineering

An Offshore Risk Analysis Method Using Fuzzy Bayesian Network

[+] Author and Article Information
J. Ren, I. Jenkinson

School of Engineering, Technology and Maritime Operations, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK

J. Wang1

School of Engineering, Technology and Maritime Operations, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UKj.wang@ljmu.ac.uk

D. L. Xu, J. B. Yang

Manchester Business School, University of Manchester, Manchester M60 1QD, UK

1

Corresponding author.

J. Offshore Mech. Arct. Eng 131(4), 041101 (Sep 04, 2009) (12 pages) doi:10.1115/1.3124123 History: Received December 13, 2005; Revised March 10, 2009; Published September 04, 2009
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The operation of an offshore installation is associated with a high level of uncertainty because it usually operates in a dynamic environment in which technical and human and organizational malfunctions may cause possible accidents. This paper proposes a fuzzy Bayesian network (FBN) approach to model causal relationships among risk factors, which may cause possible accidents in offshore operations. The FBN model explicitly represents cause-and-effect assumptions between offshore engineering system variables that may be obscured under other modeling approaches like fuzzy reasoning and Monte Carlo risk analysis. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinions when quantitative data are lacking in early design stages with a high level of innovation or when only qualitative or vague statements can be made. The model is also a modular representation of uncertain knowledge due to randomness and vagueness. This makes the risk and safety analysis of offshore engineering systems more functional and easier in many assessment contexts. A case study of the collision risk between a floating production, storage and offloading unit and the authorized vessels due to human errors during operation is used to illustrate the application of the proposed model.
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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

Grahic Jump Location
+Diagram of FBN based risk analysis
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Diagram of FBN based risk analysis
Figure 1

Diagram of FBN based risk analysis

Grahic Jump Location
+The Bayesian network structure of collision risk of FPSO and authorized vessels
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The Bayesian network structure of collision risk of FPSO and authorized vessels
Figure 2

The Bayesian network structure of collision risk of FPSO and authorized vessels

Grahic Jump Location
+Prior fuzzy probability: Pf(X=x1) and posterior fuzzy probability: Pf(X=x1∣W=w1)
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Prior fuzzy probability: Pf(X=x1) and posterior fuzzy probability: Pf(X=x1∣W=w1)
Figure 3

Prior fuzzy probability: Pf(X=x1) and posterior fuzzy probability: Pf(X=x1∣W=w1)

Grahic Jump Location
+Graphical demonstration of sensitivity analysis between Pf(X=x1∣W=w1) and Pf(X=x1)
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Graphical demonstration of sensitivity analysis between Pf(X=x1∣W=w1) and Pf(X=x1)
Figure 4

Graphical demonstration of sensitivity analysis between Pf(X=x1∣W=w1) and Pf(X=x1)

Grahic Jump Location
+Conventional BN model and marginal probabilities of all the nodes
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Conventional BN model and marginal probabilities of all the nodes
Figure 5

Conventional BN model and marginal probabilities of all the nodes

Grahic Jump Location
+Conventional BN model and posterior probabilities of all the nodes
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Conventional BN model and posterior probabilities of all the nodes
Figure 6

Conventional BN model and posterior probabilities of all the nodes

Grahic Jump Location
+A triangular distribution defined by a most likely value of 0.5, with a lower least likely value of 0.2 and upper least likely value of 0.8
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A triangular distribution defined by a most likely value of 0.5, with a lower least likely value of 0.2 and upper least likely value of 0.8
Figure 7

A triangular distribution defined by a most likely value of 0.5, with a lower least likely value of 0.2 and upper least likely value of 0.8

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