000007724 001__ 7724 000007724 005__ 20241024114710.0 000007724 02470 $$2doi$$a10.24868/issn.2631-8741.2018.011 000007724 035__ $$a2536856 000007724 037__ $$aGENERAL 000007724 245__ $$aShip diesel engine performance modelling with combined physical and machine learning approach 000007724 269__ $$a2018-10-02 000007724 336__ $$aConference Proceedings 000007724 520__ $$aCondition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.  000007724 542__ $$fCC-BY-NC-ND-4.0 000007724 6531_ $$aData-Drive methods 000007724 6531_ $$aCondition Based Maintenance 000007724 6531_ $$aGray-Box Models 000007724 6531_ $$aExhaust gas temperature prediction 000007724 6531_ $$aMachine Learning 000007724 7001_ $$aCoraddu, A$$uDepartment Of Naval Architecture, Ocean & Marine Engineering - University of Strathclyde - UK 000007724 7001_ $$aKalikatzarakis, M$$uResearch & Technology Support - Damen Schelde Naval Shipbuilding - the Netherlands 000007724 7001_ $$aOneto, L$$uDIBRIS - University of Genoa - Italy 000007724 7001_ $$aMeijn, G J$$uResearch & Technology Support - Damen Schelde Naval Shipbuilding - the Netherlands 000007724 7001_ $$aGodjevac, M$$uDepartment of Maritime & Transport Technology - Delft University of Technology - the Netherlands 000007724 7001_ $$aGeertsma, R D$$uDepartment of Maritime & Transport Technology - Delft University of Technology - the Netherlands 000007724 773__ $$tConference Proceedings of iSCSS 000007724 773__ $$jiSCSS 2018 000007724 789__ $$whttps://zenodo.org/record/2536856$$2URL$$eIsIdenticalTo 000007724 8564_ $$9a06ff792-374a-4cbb-ae78-cc3f7f8bf9d1$$s3272418$$uhttps://library.imarest.org/record/7724/files/ISCSS%202018%20Paper%20030%20Geertsma%20FINAL.pdf