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dc.contributor.authorPerera, Lokukaluge Prasad
dc.contributor.authorMo, Brage
dc.date.accessioned2017-11-02T13:58:38Z
dc.date.available2017-11-02T13:58:38Z
dc.date.created2017-02-06T13:59:45Z
dc.date.issued2017
dc.identifier.citationJournal of Offshore Mechanics and Arctic Engineering-Transactions of The Asme. 2017, 139 (2), .nb_NO
dc.identifier.issn0892-7219
dc.identifier.urihttp://hdl.handle.net/11250/2463765
dc.description.abstractThis study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering vessel performance and navigation data. The proposed data analytics is developed in the engine-propeller combinator diagram (i.e. one propeller shaft with a direct drive main engine). Three operating regions from the initial data analysis are under the combinator diagram noted to capture the shape of these regions by the proposed data analytics. The data analytics consists of implementing Gaussian Mixture Models (GMMs) to classify the most frequent operating regions of the This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering vessel performance and navigation data. The proposed data analytics is developed in the engine-propeller combinator diagram (i.e. one propeller shaft with a direct drive main engine). Three operating regions from the initial data analysis are under the combinator diagram noted to capture the shape of these regions by the proposed data analytics. The data analytics consists of implementing Gaussian Mixture Models (GMMs) to classify the most frequent operating regions of the main engine. Furthermore, the Expectation Maximization (EM) algorithm calculates the parameters of GMMs. This approach also named as a data clustering algorithm facilitates an iterative process for capturing the operating regions of the main engine (i.e. in the combinatory diagram) with the respective mean and covariance matrices. Hence, these data analytics can monitor ship performance and navigation conditions with respect to engine operating regions as a part of the SEEMP. Furthermore, development of advanced mathematical models for ship performance monitoring within the operational regions (i.e. data clusters) of marine engines is expected.nb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.no*
dc.subjectEnginesnb_NO
dc.subjectPropellersnb_NO
dc.subjectShipsnb_NO
dc.subjectNavigationnb_NO
dc.subjectVesselsnb_NO
dc.subjectAlgorithmsnb_NO
dc.titleMarine Engine-Centered Data Analytics for Ship Performance Monitoringnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber8nb_NO
dc.source.volume139nb_NO
dc.source.journalJournal of Offshore Mechanics and Arctic Engineering-Transactions of The Asmenb_NO
dc.source.issue2nb_NO
dc.identifier.doi10.1115/1.4034923
dc.identifier.cristin1447435
dc.relation.projectNorges forskningsråd: 237917nb_NO
cristin.unitcode7566,7,0,0
cristin.unitnameMaritim
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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