Bayesian network based dynamic operational risk assessment

Journal of Loss Prevention in the Process Industries

Volumn 41, Issue , 2016, Pages 399-410

  Barua, Shubharthi ^a   Gao, Xiaodan ^a   Pasman, Hans ^a   Mannan, M Sam ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

Bayesian networks; Dynamic; Fault tree; Risk assessment

Indexed keywords

BAYESIAN NETWORKS; CHEMICAL EQUIPMENT; CHEMICAL HAZARDS; CHEMICAL OPERATIONS; DYNAMICS; EQUIPMENT; HAZARDS; LEVELING (MACHINERY); OILS AND FATS; PETROCHEMICAL PLANTS; PETROCHEMICALS; RANDOM PROCESSES; RISK PERCEPTION; SAFETY TESTING; STOCHASTIC SYSTEMS;

CHEMICAL PROCESS PLANTS; CHEMICAL PROCESS SYSTEMS; DYNAMIC BAYESIAN NETWORKS; DYNAMIC CHARACTERISTICS; FAULT-TREES; RISK ASSESSMENT METHODOLOGIES; SEQUENTIAL DEPENDENCIES; TIME-DEPENDENT CHARACTERISTICS;

RISK ASSESSMENT;

EID: 84951922685     PISSN: 09504230     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jlp.2015.11.024     Document Type: Article

References (35)

1. Acosta C., Siu N. Dynamic event trees in accident sequence analysis: application to steam generator tube rupture. Reliab. Eng. Syst. Saf. 1993, 41(2):135-154.
2. Bobbio A., Portinale L., Minichino M., Ciancamerla E. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 2001, 71(3):249-260.
3. Boudali H., Dugan J.B. A discrete-time Bayesian network reliability modeling and analysis framework. Reliab. Eng. Syst. Saf. 2005, 87(3):337-349.
4. Boudali H., Dugan J.B. A continuous-time Bayesian network reliability modeling, and analysis framework. IEEE Trans. Reliab. 2006, 55(1):86-97.
5. Cacciabue P., Amendola A., Cojazzi G. Dynamic logical analytical methodology versus fault tree: the case of the auxiliary feedwater system of a nuclear power plant. Nucl. Technol. 1986, 74(2):195.
6. Celeus G., Corset F., Lannoy A., Ricard B. Designing a Bayesian network for preventive maintenance from expert opinions in a rapid and reliable way. Reliab. Eng. Syst. Saf 2006, 91(7):849-856.
7. Center for Chemical Process Safety (CCPS) Layer of protection analysissimplified process risk assessment 2001, New York, Center for Chemical Process Safety of the American Institute of Chemical Engineers.
8. Dean T., Kanazawa K. A model for reasoning about persistence and causation. Comput. Intell 1989, 5:142-150.
9. Decision Systems Laboratory GeNIe (Graphical Network Interface and SMILE (Structural Modeling, Inference, and Learning Enginer), Version 2.0 Software 2010, University of Pittsburgh, Available online at:, (accessed 22.04.12.). http://genie.sis.pitt.edu/.
10. Delic K., Mazzanti M., Strigini L. Formalising a Software Safety Case via Belief Networks 1995, Technical report, CRS, City University London.
11. Donohue S.K., Dugan J.B. Modeling the "Good enough to release" decision using V&V preference structure and Bayesian belief networks. Annual Reliab. Maintainab. Symp. 2003, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&%20arnumber=1182051.
12. Dugan J.B., Bavuso S.J., Boyd M.A. Fault trees and sequence dependencies. Proceedings of Annual Reliability and Maintainability Symposium 1990, 286-293.
13. Dugan J.B., Bavuso S.J., Boyd M.A. Dynamic fault-tree models for fault-tolerant computer systems. IEEE Trans. Reliab. 1992, 41(3):363-377.
14. Gran B.A. The Use of Bayesian Belief Networks for Combining Disparate Sources of Information in the Safety Assessment of Software Based Systems 2002, Thesis-Department of Matherical Sciences, Norwegian University of Science and Technology.
15. Hurdle E.E., Bartlett L.M., Andrews J.D. Fault diagnostics of dynamic system operation using a fault tree based method. Reliab. Eng. Syst. Saf. 2009, 94(9):1371-1380.
16. Jones B., Jenkinson I., Yang Z., Wang J. The use of Bayesian network modeling for maintenance planning in a manufacturing industry. Reliab. Eng. Syst. Saf. 2010, 95(3):267-277.
17. Khakzad N., Khan F., Amyotte P. Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf. 2011, 96:925-932.
18. Khakzad N., Khan F., Amyotte P. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. prot. January-March 2013, 91(1-2):46-53. (accessed 04.12.12.). http://dx.doi.org/10.1016/j.psep.2012.01.005.
19. Khakzad N., Khan F., Amyotte P. Risk-based design of process systems using discrete-time Bayesian networks. Reliab. Eng. Syst. Saf. 2012, 109:5-17.
20. Kjaerulff U. A computational system for dynamic time-sliced Bayesian networks. Int. J. Forecast. 1995, 11:89-101.
21. McNaught K.R., Zagorecki A. Using dynamic Bayesian networks for prognostic modeling to inform maintenance decision making. The IEEE International Conference on Industrial Engineering and Engineering Management, Hong Kong 2010.
22. Montani S., Portinale L., Bobio A. Dynamic Bayesian networks for modeling advanced fault tree features in dependability analysis. Advances in Safety and Reliability 2005, Taylor and Francis Group. Kolowrocki (Ed.).
23. Nivolianitou Z., Amendola A., Reina G. Reliability analysis of chemical processes by the DYLAM approach. Reliab. Eng. 1986, 14:163-182.
24. Nodelman U., Shelton C.R., Koller D. Continuous time Bayesian networks. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence 2002, 378-387.
25. Nodelman U., Shelton C.R., Koller D. Learning continuous time Bayesian networks. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence 2003, 421-458.
26. Nodelman U., Koller D., Shelton C.R. Expectation propagation for continuous time Bayesian networks. Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence 2005, 431-440.
27. OREDA Offshore Reliability Data 2002, OREDA participants, SINTEF.
28. Pasman H.J., Rogers W.J. BBN, a tool to make LOPA more effective, QRA more transparent and flexible, and therefore to make safety more definable. 14th Annual Symposium 2011, Mary Kay O'Connor Process Safety Center, College Station, Texas.
29. Reliability Analysis Center The applicability of Markov analysis methods to reliability, maintainability and safety. START-selected Topic in Assurance Related Technologies 2003, vol. 10(2).
30. Siu N. Risk assessment for dynamic system: an overview. Reliab. Eng. Syst. Saf. 1994, 43(1):43-73.
31. Investigation Report: Refinery Explosion and Fire, Bp Texas City, Texas, March 23, 2005 2007, U.S. Chemical Safety and Hazard Investigation Board (CSB), Report No. 2005-04-I-TX.
32. Deepwater: the Gulf Oil Disaster and the Future of Offshore Drilling 2011, U.S. National Commission on BP Deepwater Horizon Oil Spill and Offshore Drilling, Available at:, (accessed 04.09.12.). http://www.oilspillcommission.gov/final-report/.
33. Weber P., Medina-Oliva G., Simon C., Lung B. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 2012, 25(4):671-682.
34. Yang X., Mannan M.S. The development and application of dynamic operational risk assessment in oil/gas and chemical process industry. Reliab. Eng. Syst. Saf. 2010, 95:806-815.
35. Yang X., Mannan M.S. The Development and Application of Dynamic Operational Risk Assessment in Oil/gas and Chemical Process Industry 2010, PhD dissertation, Department of Chemical Engineering, Texas A&M University, College Station.