0
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
Article
References
Figures
Tables
Citing Articles

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.
FIGURES IN THIS ARTICLE
<>
Copyright © 2009 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

1
Wang, J., Sii, H. S., Yang, J. B., Pillay, A., Yu, D., Liu, J., Maistralis, E., and Saajedi, A., 2004, “Use of Advances in Technology for Marine Risk Assessment,” Risk Anal., 24 (4), pp. 1041–1063.  [CrossRef]
2
Wang, J., 2006, “Maritime Risk Assessment and its Current Status,” Qual. Reliab. Eng. Int, 22 (1), pp. 3–19.  [CrossRef]
3
Wang, J., Yang, J. B., and Sen, P., 1995, “Safety Analysis and Synthesis Using Fuzzy Set Modelling and Evidential Reasoning,” Reliab. Eng. Syst. Saf., 47 (2), pp. 103–118.  [CrossRef]
4
Yang, Z., Bonsall, S., and Wang, J., 2008, “Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritisation of Failures in FMEA,” IEEE Trans. Reliab., 57 (3), pp. 517–528.  [CrossRef]
5
Wang, J., Yang, J. B., and Sen, P., 1996, “Multi-Person and Multi-Attribute Design Evaluations Using Evidential Reasoning Based on Subjective Safety and Cost Analyses,” Reliab. Eng. Syst. Saf., 52 (2), pp. 113–129.  [CrossRef]
6
Wang, J., and Kieran, O., 2000, “Offshore Safety Assessment and safety Based Decision Making – The Current Status and Future Aspects,” ASME J. Offshore Mech. Arct. Eng., 122 (2), pp. 93–99.  [CrossRef]
7
Ren, J., Wang, J., Jenkinson, I., Xu, D. L., and Yang, J. B., 2007, “A Bayesian Network Approach for Offshore Risk Analysis Through Linguistic Variables,” China Ocean Eng., 21 (3), pp. 371–388.
8
Ren, J., Jenkinson, I., Wang, J., Xu, D. L., and Yang, J. B., 2008, “A Methodology to Model Causal Relationships on Offshore Safety Assessment Focusing on Human and Organisational Factors,” J. Safety Res., 39 (1), pp. 87–100.  [CrossRef]
9
Sii, H. S., Ruxton, T., and Wang, J., 2001, “A Synthesis Using Fuzzy Set Theory and Dempster-Shafer Based Approach to Compromise Decision Making With Multiple Attribute Applied to Risk Control Options Selection,” Proc. Inst. Mech. Eng., Part E: Journal of Process Mechanical Engineering, 212 , pp. 251–261.
10
Pearl, J., 1988, “Probabilistic Reasoning in Intelligent Systems,” "Networks of Plausible Inference", Kaufmann, Palo Alto, CA.
11
Frühwirth-Schnatter, S., 1993, “On Fuzzy Bayesian Inference,” Fuzzy Sets Syst., 60 (1), pp. 41–58.  [CrossRef]
12
Halliwell, J., Keppens, J., and Shen, Q., 2003, “Linguistic Bayesian Networks for Reasoning With Subjective Probabilities in Forensic Statistics,” Proceedings of the Fifth International Conference on AI and Law , Edinburgh, Scotland, UK, Jun. 24–28, pp. 42–50.
13
Pillay, A., and Wang, J., 2003, “Modified Failure Mode and Effects Analysis Using Approximate Reasoning,” Reliab. Eng. Syst. Saf., 79 (1), pp. 69–85.  [CrossRef]
14
Lauritzen, S. L., and Spiegelhalter, D. J., 1988, “Local Computations With Probabilities on Graphical Structures and Their Application to Expert Systems,” J. R. Stat. Soc. Ser. B (Methodol.), 50 (2), pp. 157–224.
15
Zhang, G., Bai, C., Lu, J., and Zhang, C., 2004, “Bayesian Network Based Cost Benefit Factor Inference in E-Services,” Proceedings of the Second International Conference on Information Technology for Application (ICITA) , China, Jan. 7–10, pp. 464–469.
16
Heckerman, D., and Breese, J., 1996, “Causal Independence for Probability Assessment and Inference Using Bayesian Networks,” IEEE Trans. Syst. Man Cybern., Part A. Syst. Humans, 26 (6), pp. 826–831.  [CrossRef]
17
Myllymaki, P., 2005, “Advantages of Bayesian Networks in Data Mining and Knowledge Discovery,” http://www.bayesit.com/docs/advantages.html.
18
Heckerman, D., 1997, “Bayesian Networks for Data Mining,” Data Min. Knowl. Discov., 1 (1), pp. 79–119.  [CrossRef]
19
Hayes, K. R., 1998, “Bayesian Statistical Inference in Ecological Risk Assessment,” Centre for Research on Introduced Marine Pests Report No. 17.
20
Kang, C. W., and Golay, M. W., 1999, “A Bayesian Belief Network–Based Advisory System for Operational Availability Focused Diagnosis of Complex Nuclear Power Systems,” Expert Sys. Applic., 17 (1), pp. 21–32.  [CrossRef]
21
Karwowski, W., and Mital, A., 1986, “Potential Applications of Fuzzy Sets in Industrial Safety Engineering,” Fuzzy Sets Syst., 19 (2), pp. 105–120.  [CrossRef]
22
Zadeh, L. A., 1984, “Fuzzy Probabilities,” Inf. Process. Manage., 20 , pp. 363–372.  [CrossRef]
23
Chou, K. G., and Yuan, J., 1993, “Fuzzy-Bayesian Approach to Reliability of Existing Structures,” J. Struct. Eng., 119 (11), pp. 3276–3290.  [CrossRef]
24
Pericchi, L. R., and Walley, P., 1991, “Robust Bayesian Credible Intervals and Prior Ignorance,” Int. Statist. Rev., 59 (1), pp. 1–23.
25
Yang, C. C., and Cheung, K. M., 1995, “Fuzzy-Bayesian analysis With Continuous-Valued Evidence,” Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics , Vancouver, Canada, Oct. 22–25, pp. 441–446.
26
León-Rojas, J. M., Masero, V., and Morales, M., 2003, “On the Fuzzy Bayesian Inference of Population Annoyance Level Caused by Noise Exposure,” Symposium on Applied Computing, Proceedings of the 2003 ACM symposium on Applied Computing , Melbourne, FL, Mar. 9–12, pp. 227–234.
27
Eleye-Datubo, A., Wall, A., Saajedi, A., and Wang, J., 2008, “Marine and Offshore Safety Assessment by Incorporative Risk Modelling in a Fuzzy-Bayesian Network of an Induced Mass Assignment Paradigm,” Risk Anal., 28 (1), pp. 95–112.  [CrossRef]
28
Darwiche, A., 2003, “A Differential Approach to Inference in Bayesian Networks,” J. ACM, 50 (3), pp. 280–305.  [CrossRef]
29
Li, H. L., and Kao, H. Y., 2005, “Constrained Abductive Reasoning With Fuzzy Parameters in Bayesian Networks,” Comput. Oper. Res., 32 (1), pp. 87–105.  [CrossRef]
30
Halliwell, J., and Shen, Q., 2002, “Towards a Linguistic Probability Theory,” Proceedings of the 11th International Conference on Fuzzy Sets and Systems (FUZZ-IEEE ’02) , Honolulu, HI, May 16–17, pp. 596–601.
31
Buckley, J. J., 2002, "An Introduction to Fuzzy Logic and Fuzzy Sets", Springer-Verlag, Heidelberg.
32
Mitchell, K., and Bright, C. K., 1995, “Minimizing the Potential for Human Error in Ship Operation,” IMAS 95, Management and Operation of Ships, Practical Techniques for Today and Tomorrow .
33
Jenson, F. V., 1993, "Introduction to Bayesian Networks: HUGIN", Aalborg University Press, Aalborg, Denmark.
34
NETICA , 2002, “Netica-J Reference Manual-Version 2.21,” Java Version of Netica API, Norsys Software Corporation.
35
Kadie, C. M., Hovel, D., and Horvitz, E., 2001, “MSBNx: A Component-Centric Toolkit for Modelling and Inference With Bayesian Networks,” Microsoft Research Technical Report No. MSR-TR-2001-67.
36
Moore, R. E., 1966, "Interval Analysis", Prentice-Hall, Englewood Cliffs, NJ.
37
OREDA (Offshore Reliability Data), 1997, "OREDA: Offshore Reliability Data Handbook", 3rd ed., Det Norske, Veritas Industri Norge as DNV Technica, Norway.
38
Yang, J. B., Liu, J., Wang, J., Sii, H. S., and Wang, H. W., 2006, “Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-RIMER,” IEEE Trans. Syst. Man Cybern., Part A. Syst. Humans, 36 (2), pp. 266–285.  [CrossRef]

Figures

Grahic Jump Location
+Diagram of FBN based risk analysis
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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)
View Large  |  Save Figure  |  Download Slide (.ppt)
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)
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. | Accept
Sign In