UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network

Riyadi, S; Utama, IKAP; Aryawan, WD; Rulaningtyas, R; Thomas, GA; (2020) An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network. In: MASTIC 2020 Proceedings. (pp. 012054). IOP Science: Surabaya, Indonesia. Green open access

[thumbnail of Riyadi_2020_IOP_Conf._Ser.__Earth_Environ._Sci._557_012054.pdf]
Preview
Text
Riyadi_2020_IOP_Conf._Ser.__Earth_Environ._Sci._557_012054.pdf - Published version

Download (2MB) | Preview

Abstract

Estimating shaft power of a crew boat is very important to be analysed because it has high-speed operational characteristics along with limited routes. To understand the phenomena, 3 sister crew-boats with operational distance about 40-60 nautical miles every day are investigated. The daily operational time is 8 hours and the configurations are: 4.04% full speed, 13.63% economical speed, 1.81% slow speed, 7.65% snatching, 1.25% manoeuvring, 5.29% idle, and the remaining time is in standby condition. The crew boats are fitted with a monitoring system namely SHIMOS®, in which data is sent to a server in the centre office every 2 minutes. The data consists of time capture, boat position (latitude and longitude), speed, left and right RPM engine, left and right flow-meter data engine, and average of fuel consumption data in everyday operation. Three years of data has been collected for the vessel. The present study proposed characteristics of crew-boat shaft power, which affected by external factors using Artificial Neural Network (ANN) back propagation method and optimisation in 4 hidden layers and 40 neurons with relative error 6.2%. The results demonstrates good agreement with previous popular method that using statistical models.

Type: Proceedings paper
Title: An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network
Event: 2nd Maritime Safety International Conference (MASTIC) 18-Jul-20
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1755-1315/557/1/012054
Publisher version: http://dx.doi.org/10.1088/1755-1315/557/1/012054
Language: English
Additional information: © Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10118212
Downloads since deposit
16Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item