NDLI: Use of Hybrid Causal Logic Method for Preliminary Hazard Analysis of Maritime Autonomous Surface Ships

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Use of Hybrid Causal Logic Method for Preliminary Hazard Analysis of Maritime Autonomous Surface Ships

Content Provider	MDPI
Author	Zhang, Di Han, Zhepeng Zhang, Kai Zhang, Jinfen Zhang, Mingyang Zhang, Fan
Copyright Year	2022
Description	Recently, the safety issue of maritime autonomous surface ships (MASS) has become a hot topic. Preliminary hazard analysis of MASS can assist autonomous ship design and ensure safe and reliable operation. However, since MASS technology is still at its early stage, there are not enough data for comprehensive hazard analysis. Hence, this paper attempts to combine conventional ship data and MASS experiments to conduct a preliminary hazard analysis for autonomy level III MASS using the hybrid causal logic (HCL) method. Firstly, the hazardous scenario of autonomy level III MASS is developed using the event sequence diagram (ESD). Furthermore, the fault tree (FT) method is utilized to analyze mechanical events in ESD. The events involving human factors and related to MASS in the ESD are analyzed using Bayesian Belief Network (BBN). Finally, the accident probability of autonomy level III MASS is calculated in practice through historical data and a test ship with both an autonomous and a remote navigation mode in Wuhan and Nanjing, China. Moreover, the key influence factors are found, and the accident-causing event chains are identified, thus providing a reference for MASS design and safety assessment process. This process is applied to the preliminary hazard analysis of the test ship.
Starting Page	725
e-ISSN	20771312
DOI	10.3390/jmse10060725
Journal	Journal of Marine Science and Engineering
Issue Number	6
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2022-05-25
Access Restriction	Open
Subject Keyword	Journal of Marine Science and Engineering Marine Engineering Maritime Autonomous Surface Ships Hybrid Causal Logic Preliminary Hazard Analysis Risk Assessment Hazard Identification
Content Type	Text
Resource Type	Article

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