Risk evolution from causes to consequences of engine room fires on ships by mapping bow-tie into fuzzy Bayesian network

Abstract

An integrated quantitative risk analysis methodology based on the BT (bow-tie) and FBN (fuzzy Bayesian network) is developed in the present study to evaluate the risk evolution from causes to consequences of engine room fires on ships. Given limited historical statistics on engine room fire accidents, a probabilistic estimation model based on fuzzy sets is employed to determine prior probabilities of BN nodes. A case study of the engine room fire is carried out to demonstrate the soundness and applicability of the proposed methodology. Moreover, scenario analysis is implemented to assess how safety barriers can mitigate escalation of consequences triggered by engine room fires, while sensitivity analysis ranks critical hazards and quantifies correlations between different consequences and risk events within engine room fires. The results indicate that mechanical faults, improper bunker operation, improper maintenance, circuit overload or short circuit and insulation aging or damage are the main direct risk events triggering engine room fires on ships. The effectiveness of safety barrier (fire extinguishing devices) can play a decisive role in preventing further escalation of controlled fire incidents.

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