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

Andrea Coraddu*, Luca Oneto, Francesca Cipollini, Miltos Kalikatzarakis, Gert Jan Meijn, Rinze Geertsma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
29 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1360-1381
JournalShips and Offshore Structures
Issue number6
Publication statusPublished - 2021


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


Dive into the research topics of 'Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions'. Together they form a unique fingerprint.

Cite this