Integrating α-cut interval based fuzzy fault tree analysis with Bayesian network for criticality analysis of submarine pipeline leakage: A novel approach

Submarine pipelines are the major transportation mode of marine oil and gas resources. Because of submarine pipeline damage, the leakage of oil and gas will result the serious consequences such as environmental disasters, fires and explosions, and huge economic losses. There are variously internal and external factors that initiate spill accidents of oil and gas. To prevent and mitigate such accidents, risk analysis is an efficient way. Fault tree analysis is an effective tool to identify failure causes and perform the risk assessment. In fault tree analysis, it is presumed that all basic events are statistically independent and have precise occurrence probabilities. In the case, when probability data of basic events of system fault tree are unavailable or imprecise, the concept of fuzzy set theory and expert elicitation is used to obtain qualitative data. In the quantification of qualitative data, experts' knowledge is used which may raise issues such as incompleteness, imprecision, and lack of consensus. In the process to minimize the uncertainty, expert's opinions are aggregated and updated to the posterior possibilities using the prior observations. Bayesian networks have the advantages of representing the dependencies of events, updating probabilities, and dealing with uncertainties. In this research paper, a novel methodology is proposed by combining fuzzy fault tree analysis and Bayesian network to obtain updated prior possibilities of basic events and top event of system fault tree when new information are available. The main contributions of this research are: weakest t-norm based arithmetic operations on fuzzy numbers are employed for less uncertainty accumulation during the process; weakest t-norm and α-cut based similarity aggregation method is developed to evaluate the possibilities of basic events and top event in system fuzzy fault tree analysis; the obtained prior possibilities are then updated using fuzzy Bayesian network; criticality analysis is executed using the posterior possibilities of basic events and top event. Further, a case study of leakage in submarine pipeline is discussed to demonstrate the applicability and effectiveness of the proposed methodology. The obtained results are then compared with the pre-existing results which shows the validity and applicability of the proposed method.