Print Email Facebook Twitter Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions Title Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions Author Coraddu, A. (University of Strathclyde) Oneto, Luca (University of Genova) Cipollini, Francesca (University of Genova) Kalikatzarakis, Miltos (University of Strathclyde; Damen Schelde Naval Shipbuilding) Meijn, Gert Jan (Damen Schelde Naval Shipbuilding) Geertsma, R.D. (TU Delft Ship Design, Production and Operations; Netherlands Defence Academy) Date 2021 Abstract Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel. Subject condition monitoringexhaust gas temperaturesfeature mappinghybrid modelsKernel methodsmultitask learning To reference this document use: http://resolver.tudelft.nl/uuid:3fbf3dc4-7feb-44f2-9dac-e7e20758f027 DOI https://doi.org/10.1080/17445302.2021.1920095 ISSN 1744-5302 Source Ships and Offshore Structures, 17 (6), 1360-1381 Part of collection Institutional Repository Document type journal article Rights © 2021 A. Coraddu, Luca Oneto, Francesca Cipollini, Miltos Kalikatzarakis, Gert Jan Meijn, R.D. Geertsma Files PDF 17445302.2021_1.pdf 6.65 MB Close viewer /islandora/object/uuid:3fbf3dc4-7feb-44f2-9dac-e7e20758f027/datastream/OBJ/view