Risk assessment of general FPSO supply system based on hybrid fuzzy fault tree and Bayesian network

One of the major risks to the operation of a FPSO (Floating, Production, Storage and Offloading) is collision of supply vessels. Experiences have shown that collisions of supply vessel take place frequently, although the consequences are limited. The offshore oil&gas industry has established relevant design standards and operational procedures, which are proven to be effective in reducing this risk. As the risk assessment becomes increasingly applied, there is a need to quantify the likelihood of supply vessel collisions especially during the early design/project stage when historical incident data does not exist. This paper proposes a method for estimating the likelihood of supply vessel collision based on fault tree analysis (FTA), Bayesian network (BN), hybrid theory and fuzzy theory. In lieu of using historical data, this method combines the fuzzy logic with expert heuristics to determine the basic events(BEs). The triangular fuzzy membership functions and a hybrid theory were adopted to define factors that lack data or had no data in the events. To account for the imprecise information of human error, a scheme is devised to use the fuzzy fault probability(FFP) based on the hybrid theory, or a hybrid fuzzy fault tree analysis (HFFT). The algorithm transforms the FT into a BN with a conditional probability table (CPT). This new method was demonstrated in an example of a FPSO, and the results were has been validated through case studies and accident reports. A sensitivity analysis was also conducted to illustrate key parameters.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.118767
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S002980182402105X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zong, Shuai
  • Wang, Zili
  • Liu, Kun
  • Wang, George
  • Lu, Yue
  • Huang, TianBo
• Publication Date: 2024-11-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 118767
• Serial:
  • Ocean Engineering
  • Volume: 311
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Bayes' theorem; Fault tree analysis; Fuzzy logic; Maritime safety; Risk assessment; Ships
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01929134
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2024 5:20PM