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A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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21
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Accident Analysis & Prevention, Volume 194
Abstract
With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships’ (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems’ availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.
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Funding Information: The research was supported by the Hubei Provincial Natural Science Foundation of China (2019CFA039), the National Natural Science Foundation of China (51920105014; 52071247), and the Innovation and entrepreneurship team import project of Shaoguan city (201212176230928). Publisher Copyright: © 2023 Elsevier Ltd
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Han, Z, Zhang, D, Fan, L, Zhang, J & Zhang, M 2024, 'A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships', Accident Analysis & Prevention, vol. 194, 107342. https://doi.org/10.1016/j.aap.2023.107342