Operational profiles data analytics for ship design improvement

A. Coraddu, T. Cleophas, K. Xepapa, L. Oneto, D. Anguita

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

The main purpose of thiswork is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. Data driven models are built upon statistical inference procedures based on the historical data collection. The advantage of these methods is that there is no need of any a-priory knowledge of the underlying physical system. Furthermore, thanks to the nature of these approaches, it is possible to exploit even data from sensors that could contain some kind of hidden information that cannot be easily extracted with a parametric approach. The use of these tools is nowadays made possible since such information is digitally available from different sources: (i) data stored on board of vessels; (ii) Automatic Identification System (AIS) data available through the internet. A data driven modelling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel’s state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship’s performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.

LanguageEnglish
Title of host publicationProceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016
EditorsC Guedes Soares, T A Santos
Place of PublicationLondon
Pages635-646
Number of pages12
Volume1
Publication statusPublished - 21 Jun 2016
Event3rd International Conference on Maritime Technology and Engineering, MARTECH 2016 - Lisbon, Portugal
Duration: 4 Jul 20166 Jul 2016

Conference

Conference3rd International Conference on Maritime Technology and Engineering, MARTECH 2016
CountryPortugal
CityLisbon
Period4/07/166/07/16

Fingerprint

ship design
Ships
Automation
vessel
Sensors
Machinery
Data structures
Data acquisition
Identification (control systems)
automation
Internet
sensor
machinery
data acquisition

Keywords

  • ship design
  • data analysis
  • operating profiles

Cite this

Coraddu, A., Cleophas, T., Xepapa, K., Oneto, L., & Anguita, D. (2016). Operational profiles data analytics for ship design improvement. In C. G. Soares, & T. A. Santos (Eds.), Proceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016 (Vol. 1, pp. 635-646). London.
Coraddu, A. ; Cleophas, T. ; Xepapa, K. ; Oneto, L. ; Anguita, D. / Operational profiles data analytics for ship design improvement. Proceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016. editor / C Guedes Soares ; T A Santos. Vol. 1 London, 2016. pp. 635-646
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Coraddu, A, Cleophas, T, Xepapa, K, Oneto, L & Anguita, D 2016, Operational profiles data analytics for ship design improvement. in CG Soares & TA Santos (eds), Proceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016. vol. 1, London, pp. 635-646, 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016, Lisbon, Portugal, 4/07/16.

Operational profiles data analytics for ship design improvement. / Coraddu, A.; Cleophas, T.; Xepapa, K.; Oneto, L.; Anguita, D.

Proceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016. ed. / C Guedes Soares; T A Santos. Vol. 1 London, 2016. p. 635-646.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N2 - The main purpose of thiswork is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. Data driven models are built upon statistical inference procedures based on the historical data collection. The advantage of these methods is that there is no need of any a-priory knowledge of the underlying physical system. Furthermore, thanks to the nature of these approaches, it is possible to exploit even data from sensors that could contain some kind of hidden information that cannot be easily extracted with a parametric approach. The use of these tools is nowadays made possible since such information is digitally available from different sources: (i) data stored on board of vessels; (ii) Automatic Identification System (AIS) data available through the internet. A data driven modelling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel’s state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship’s performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.

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Coraddu A, Cleophas T, Xepapa K, Oneto L, Anguita D. Operational profiles data analytics for ship design improvement. In Soares CG, Santos TA, editors, Proceedings of 3rd International Conference on Maritime Technology and Engineering, MARTECH 2016. Vol. 1. London. 2016. p. 635-646