Nowadays, LNG Import Terminals (where the storage and regasification process is conducted) are mostly onshore; the construction of these terminals is costly and many adaptations are necessary to abide by environmental and safety laws. Moreover, an accident in one of these plants might produce considerable impact in neighboring areas and population; this risk may be even worse due to the possibility of a terrorist attack. Under this perspective, a discussion is conducted about a vessel known as FSRU (Floating Storage and Regasification Unit), which is a storage and regasification offshore unit, that can work miles away from de coast and, owing to this, can be viewed as an option for LNG storage and regasification facilities. The goal is to develop a method for using Bayesian Networks in the Risk Analysis of Regasification System of the FSRU, which will convert Fault Trees (FT) into Bayesian Networks (BN) providing more accurate data. Using BN is possible to represent uncertain knowledge and local conditional dependencies. In addition, FT models the failure modes as independent and binary events while BN may model a larger number of states. It is worth noting that BN does not require the determination of cut sets; however, given a failure, it is capable of providing the probability of each possible cut set. This method will provide information to define, in a future study, a maintenance plan based on the Reliability Centered Maintenance. The results intend to clarify the applicability of BN on risk assessment and might improve the risk analysis of a Regasification System FSRU.

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