Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks

Abstract

Accidents caused by steering gear malfunctions, especially during port berthing maneuvers, the strait, and canal crossings, can lead to hazardous consequences on the environment and human life. This study aims to provide the risk evaluation and investigation of the root causes of the steering gear failures on board using Fuzzy Bayesian Networks. To determine the effects of root causes on steering gear failures, the Bayesian Network was built in the NETICA software. Running several scenarios on the network are ensured the investigation of how the root reasons affect the problem. Prior and conditional probabilities obtained from meetings and interviews with six different experts were used to apply Bayesian inference. Sensitivity analysis, forward propagation analysis by applying the best- and worst-case scenarios, and validation of the network were conducted. Results depicted that the functionality of the electrical and mechanical line components is essential to preventing the breakdown of the steering gear system. The most significant contributor to component-related electrical failures is loose or corroded wiring and connections, whereas hydraulic oil-sourced errors have a significant impact on equipment wear and malfunction. The probability of a steering gear accident is assessed to be 13.7% under the best-case scenario and 79.1% in the worst-case scenario.