000007750 001__ 7750
000007750 005__ 20240626123055.0
000007750 02470 $$2doi$$a10.24868/issn.2631-8741.2020.011
000007750 035__ $$a4468482
000007750 037__ $$aGENERAL
000007750 245__ $$aPredicting Future of Unattended Machinery Plants: A Step Toward Reliable Autonomous Shipping
000007750 269__ $$a2020-10-05
000007750 336__ $$aConference Proceedings
000007750 520__ $$aFuture waterborne transport operations in short-sea, sea-river, and inland waterways can be performed by autonomous vessels. The automation of maritime shipping directly emphasizes reducing crew numbers, minimizing operational costs, andmitigating human error during the operation. Recent researches have focused on understanding autonomous navigation while the reliability of unattended machinery plant has received very little attention. This paper aims at developing a method for predicting the performance of failure-sensitive components that may be left unattended in autonomous shipping. The presented methodology adopts Bayesian Inference as the basis of the artificial intelligence for predicting maintenance schedules including repair, inspection, and irregular checks of unattended systems. A Multinomial Process Tree (MPT) is used to model the failures within the system, identify faulty components, and to predict their failure times. A real case study from a short sea voyage is adopted to demonstrate the application of the presented methodology. The results of this research will assist decision and policy-makers to prevent costly failures in Maritime Autonomous Surface Ships (MASS) and extend the service life of autonomous systems before any human intervention.
000007750 542__ $$fCC-BY-4.0
000007750 6531_ $$aAutonomous Shipping
000007750 6531_ $$aReliability Engineering
000007750 6531_ $$aMultinomial Process Tree
000007750 6531_ $$aBayesian Inference Unattended Machinery
000007750 7001_ $$aAbaei, MM$$uTechnical University of Delft, Netherlands;
000007750 7001_ $$aBahooToroody, A$$uUniversity of Strathclyde, UK
000007750 7001_ $$aArzaghi, E$$uQueensland University of Technology, Australia
000007750 773__ $$tConference Proceedings of iSCSS
000007750 773__ $$jiSCSS 2020
000007750 789__ $$whttps://zenodo.org/record/4468482$$2URL$$eIsIdenticalTo
000007750 85641 $$uhttps://www.imarest.org/events/inec-2020/iscss-2020$$yConference website
000007750 8564_ $$91a200c6b-cd6b-4b24-b0f1-a0b88dc66f06$$s1128478$$uhttps://library.imarest.org/record/7750/files/iSCSS_2020_Paper_23.pdf