Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

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)

2021

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.

condition monitoring
exhaust gas temperatures
feature mapping
hybrid models
Kernel methods
multitask learning

http://resolver.tudelft.nl/uuid:3fbf3dc4-7feb-44f2-9dac-e7e20758f027

https://doi.org/10.1080/17445302.2021.1920095

1744-5302

Ships and Offshore Structures, 17 (6), 1360-1381

Institutional Repository

journal article

© 2021 A. Coraddu, Luca Oneto, Francesca Cipollini, Miltos Kalikatzarakis, Gert Jan Meijn, R.D. Geertsma