Bayesian network modelling of an offshore electrical generation system for applications within an asset integrity case for normally unattended offshore installations

Abstract

This article proposes the initial stages of the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore system. The main focus is the construction of a Bayesian network model that demonstrates the interactions of multiple offshore safety critical elements to analyse asset integrity. The majority of the data required to complete the Bayesian network was gathered from various databases and past risk assessment experiments and projects. However, where data were incomplete or non-existent, expert judgement was applied through pairwise comparison, analytical hierarchy process and a symmetric method to fill these data gaps and to complete larger conditional probability tables. A normally unattended installation–Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed to ensure safe operations in situations whereby physical human-to-machine interaction is not occurring. The Integrity Case can be said to be dynamic as it shall be continually updated for an installation as the quantitative risk analysis data are recorded. This allows for the integrity of the various systems and components of an offshore installation to be continually monitored. The Bayesian network allows cause and effect relationships to be modelled through clear graphical representation. The model accommodates for continual updating of failure data.

Keywords

Offshore safety Integrity Case Bayesian networks offshore installations electrical generation systems

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References

RMRI Plc. RMRI’s asset integrity case. Report, RMRI Plc, Manchester, 2011.

Wang

Offshore safety case approach and formal safety assessment of ships. J Safety Res 2002; 33: 81–115.

Department of Energy. The public inquiry into the Piper Alpha disaster. Report, Department of Energy, London, 1990.

Health and Safety Executive (HSE). The offshore installations (safety case) regulations. Report, HSE, London, 1992.

Health and Safety Executive (HSE). A guide to the offshore installations (safety case) regulations 2005. Report, HSE, London, 2006.

Health and Safety Executive (HSE). Guidance for risk assessment for offshore installations. Report, HSE, London, 2006.

Cai

Liu

. Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations. Risk Anal 2013; 33: 1293–1311.

Health and Safety Executive (HSE). The offshore installations and wells (design and construction, etc.) regulations. Report, HSE, London, 1996.

Auld

A safety case development framework. Report, Atomic Weapons Establishment & Defence Science and Technology Laboratory, Bristol, 2013.

10.

Risktec. Safety cases for the offshore wind industry. RiskWorld, 2013, http://www.risktec.tuv.com/media/252677/riskworld%2024_lr.pdf

11.

Cockram

Lockwood

Electronic safety cases: challenges and opportunities. Report, Praxis Critical Systems, Bath, 2003.

12.

Khakzad

Khan

Amyotte

Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab Eng Syst Safe 2011; 96: 925–932.

13.

Yang

Mannan

MS.

The development and application of dynamic operational risk assessment in oil/gas and chemical process industry. Reliab Eng Syst Safe 2010; 95: 806–815.

14.

Matellini

Wall

Jenkinson

. Modelling dwelling fire development and occupancy escape using Bayesian network. Reliab Eng Syst Safe 2013; 114: 75–91.

15.

Jones

. The 2010 gulf coast oil spill, http://BookBoon.com/ (2010, accessed December 2014).

16.

Eleye-Datubo

Wall

Saajedi

. Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Anal 2006; 26: 695–721.

17.

Fenton

Neil

Risk assessment and decision analysis with Bayesian networks. 1st ed. Oxford: Taylor & Francis, 2013.

18.

Bolstad

WM.

Introduction to Bayesian statistics. 2nd ed.Hoboken, NJ: John Wiley & Sons, 2007.

19.

Neapolitan

RE.

Learning Bayesian networks. Upper Saddle River, NJ: Pearson/Prentice Hall, 2007.

20.

Wang

The limitation of Bayesianism. Artif Intell 2004; 158: 97–106.

21.

Neil

Fenton

Nielson

Building large-scale Bayesian networks. Knowl Eng Rev 2000; 15: 257–284.

22.

RMRI Plc. Assessment of risks associated with the alternator rotor end cap disintegration on the Thistle Alpha platform. Report, Petrofac Facilities Management Ltd, Manchester, 2009.

23.

US Nuclear Regulatory Commission. Potential generator missiles – generator rotor retaining rings. Resolution of Generic Safety Issues Report, US Nuclear Regulatory Commission, Rockville, MA, August 2008.

24.

Zhang

Sekhari

Ouzrout

. Deriving consistent pairwise comparison matrices in decision making methodologies based on linear programming method. J Intell Fuzzy Syst 2014; 27: 1977–1989.

25.

Lin

Kou

Bayesian revision of the individual pair-wise comparison matrices under consensus in AHP-GDM. Appl Soft Comput 2015; 35: 802–811.

26.

Saaty

The analytical hierarchy process. New York: McGraw-Hill, 1980.

27.

Merkin

Group choice. New York: John Wiley & Sons, 1979.

28.

Saaty

How to make a decision: the analytic hierarchy process. Eur J Oper Res 1990; 48: 9–26.

29.

Saaty

Fundamentals of decision making. Pittsburgh, PA: RWS Publication, 1994.

30.

Koczkodaj

Szybowski

Pairwise comparison simplified. Appl Math Comput 2015; 253: 387–394.

31.

Ahmed

., 2005. Application of Analytical Hierarchy Process and Bayesian Belief Networks for Risk analysis. Complexity International, Volume 12, pp. 1–10.

32.

Donegan

Dodd

A note on Saaty’s random indexes. Math Comput Modell 1991; 15: 135–137.

33.

Das

Generating conditional probabilities for Bayesian networks: easing the knowledge acquisition problem. Report, Command and Control Division, DSTO, Edinburgh, SA, Australia, August 2008.

34.

Brito

Griffiths

A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions. Reliab Eng Syst Safe 2016; 146: 55–67.