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Capturing and analysing real-time data from TUGS

Capturing and analysing real-time data from TUGS
Capturing and analysing real-time data from TUGS

Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.

The American Society of Mechanical Engineers
Lim, Serena
a63d31ff-97a7-4de6-88de-21b6e046ed49
Pazouki, Kayvan
1e69a646-83da-49ce-af3a-c40808c83ffe
Murphy, Alan J.
8e021dad-0c60-446b-a14e-cddd09d44626
Zhang, Ben
41040223-e5fb-4fe7-86ce-f25eecae38a3
Lim, Serena
a63d31ff-97a7-4de6-88de-21b6e046ed49
Pazouki, Kayvan
1e69a646-83da-49ce-af3a-c40808c83ffe
Murphy, Alan J.
8e021dad-0c60-446b-a14e-cddd09d44626
Zhang, Ben
41040223-e5fb-4fe7-86ce-f25eecae38a3

Lim, Serena, Pazouki, Kayvan, Murphy, Alan J. and Zhang, Ben (2018) Capturing and analysing real-time data from TUGS. In Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering: Ocean Engineering. vol. 7B, The American Society of Mechanical Engineers. 9 pp . (doi:10.1115/OMAE2018-78003).

Record type: Conference or Workshop Item (Paper)

Abstract

Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.

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More information

Published date: 25 September 2018
Additional Information: Funding Information: This work was conducted in collaboration with Royston Ltd and was funded by Innovate UK within the Whole Vessel Energy Management Project (project reference 102431). Authors would also like to thank Shervin Younessi and team who contributed significantly in project development of sensor communication on-board vessels.
Venue - Dates: ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2018, , Madrid, Spain, 2018-06-17 - 2018-06-22

Identifiers

Local EPrints ID: 484046
URI: http://eprints.soton.ac.uk/id/eprint/484046
PURE UUID: 5a51bf6b-eb9e-4335-96a1-9ec4796054ac

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Date deposited: 09 Nov 2023 17:45
Last modified: 10 May 2024 17:03

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Contributors

Author: Serena Lim
Author: Kayvan Pazouki
Author: Alan J. Murphy
Author: Ben Zhang

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