An integrated approach of intuitionistic fuzzy fault tree and Bayesian network analysis applicable to risk analysis of ship mooring operations

Mooring is a technique to anchor the ship to a fixed or drifting component and make it associated while loading and unloading operations are in process. In the risk assessment of ship mooring operation, it is hard to get the precise failure data of system components. In this study, an integrated intuitionistic fuzzy fault tree and Bayesian network-based method is proposed for system failure probability evaluation in case of imprecise and insufficient failure data. Root causes of the 'parted rope injury during ship mooring operation' are obtained using fault tree analysis. The improved similarity aggregation method based intuitionistic fuzzy fault tree analysis is proposed to better deal with uncertainty. To update the failure probabilities for new evidences, the Bayesian network model is constructed. Importance ranking to the basic events of the system fault tree is given using the 'Fussell-Vesely importance' measure to identify the contribution of each basic event in system failure. The results demonstrate that the proposed approach is an alternative for probabilistic reliability evaluation when the components' statistical failure data is not precisely available. This will help decision-makers and operators in taking decisions for preventive and corrective actions in the risk management process.

Automated summary

This study proposes an integrated intuitionistic fuzzy fault tree and Bayesian network-based method for evaluating system failure probability when failure data is imprecise and insufficient. The root causes of 'parted rope injury during ship mooring operation' are identified using fault tree analysis. The proposed method effectively deals with uncertainty and updates failure probabilities using a Bayesian network model. The results demonstrate that this approach is a viable alternative for probabilistic reliability evaluation when statistical failure data is not precise.