TY - JOUR
T1 - An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network
AU - Riyadi, S.
AU - Utama, I. K.A.P.
AU - Aryawan, W. D.
AU - Rulaningtyas, R.
AU - Thomas, G. A.
N1 - Funding Information:
The research is funded under a Doctoral Research Grant. Therefore, authors wish to express their sincere gratitude to the Ministry of Research and Technology (Kemristek) BRIN and ITS Research Institution (DRPM) for providing the Research Grant under contract number 1238/PKS/ITS/2020. Further, authors wished to thank PT Pelayaran Nasional Ekalya Purnamasari (PNEP) to providing data vessel monitoring.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/9/14
Y1 - 2020/9/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091994229&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/557/1/012054
DO - 10.1088/1755-1315/557/1/012054
M3 - Conference article
AN - SCOPUS:85091994229
SN - 1755-1307
VL - 557
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012054
T2 - 2nd Maritime Safety International Conference, MASTIC 2020
Y2 - 18 July 2020
ER -