Vessel monitoring and design in industry 4.0: a data driven perspective

Luca Oneto, Davide Anguita, Andrea Coraddu, Toine Cleophas, Katerina Xepapa

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

2 Citations (Scopus)

Abstract

The main purpose of this work 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. A data driven modeling 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 publication2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)9781509011315
DOIs
Publication statusPublished - 9 Nov 2016
Event2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016 - Bologna, Italy
Duration: 7 Sep 20169 Sep 2016

Conference

Conference2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016
CountryItaly
CityBologna
Period7/09/169/09/16

Fingerprint

vessels
Automation
industries
monitoring
automation
industry
Monitoring
Machinery
performance
Data structures
Industry
Data acquisition
Ships
data acquisition
prognosis
Sensors
machinery
ships
profiles
maintenance

Keywords

  • marine vehicles
  • data models
  • engines
  • radio frequency
  • monitoring
  • propellers
  • ports

Cite this

Oneto, L., Anguita, D., Coraddu, A., Cleophas, T., & Xepapa, K. (2016). Vessel monitoring and design in industry 4.0: a data driven perspective. In 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016 [7740594] Piscataway, NJ: IEEE. https://doi.org/10.1109/RTSI.2016.7740594
Oneto, Luca ; Anguita, Davide ; Coraddu, Andrea ; Cleophas, Toine ; Xepapa, Katerina. / Vessel monitoring and design in industry 4.0 : a data driven perspective. 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016. Piscataway, NJ : IEEE, 2016.
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Oneto, L, Anguita, D, Coraddu, A, Cleophas, T & Xepapa, K 2016, Vessel monitoring and design in industry 4.0: a data driven perspective. in 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016., 7740594, IEEE, Piscataway, NJ, 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016, Bologna, Italy, 7/09/16. https://doi.org/10.1109/RTSI.2016.7740594

Vessel monitoring and design in industry 4.0 : a data driven perspective. / Oneto, Luca; Anguita, Davide; Coraddu, Andrea; Cleophas, Toine; Xepapa, Katerina.

2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016. Piscataway, NJ : IEEE, 2016. 7740594.

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

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Oneto L, Anguita D, Coraddu A, Cleophas T, Xepapa K. Vessel monitoring and design in industry 4.0: a data driven perspective. In 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016. Piscataway, NJ: IEEE. 2016. 7740594 https://doi.org/10.1109/RTSI.2016.7740594