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

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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.
Original languageEnglish
Number of pages22
JournalShips and Offshore Structures
Early online date13 May 2021
DOIs
Publication statusE-pub ahead of print - 13 May 2021

Keywords

  • kernal method
  • feature mapping
  • multitask learning
  • condition monitoriing
  • exhaust gas temperatures

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