Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Letting losses be lessons: Human-machine cooperation in maritime transport

Fan, S, Shi, K, Weng, J and Yang, Z (2024) Letting losses be lessons: Human-machine cooperation in maritime transport. Reliability Engineering and System Safety, 253. ISSN 0951-8320

[img]
Preview
Text
Letting losses be lessons Human-machine cooperation in maritime transport.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

Item Type: Article
Uncontrolled Keywords: Maritime safety; Human factors; Seafarer competency; Human-machine system; Accident investigation; Transportation engineering; 01 Mathematical Sciences; 09 Engineering; 15 Commerce, Management, Tourism and Services; Strategic, Defence & Security Studies
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Engineering
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 10 Jan 2025 11:03
Last Modified: 10 Jan 2025 11:03
DOI or ID number: 10.1016/j.ress.2024.110547
URI: https://researchonline.ljmu.ac.uk/id/eprint/25235
View Item View Item