Proceedings of the International Ship Control Systems Symposium (iSCSS)
Published October 2, 2018 | Version v1
Conference paper Open

Ship diesel engine performance modelling with combined physical and machine learning approach

Creators

  • 1. Department Of Naval Architecture, Ocean & Marine Engineering - University of Strathclyde - UK
  • 2. Research & Technology Support - Damen Schelde Naval Shipbuilding - the Netherlands
  • 3. DIBRIS - University of Genoa - Italy
  • 4. Department of Maritime & Transport Technology - Delft University of Technology - the Netherlands

Description

Condition 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%. 

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References

  • Coraddu, A., Oneto, L., Baldi, F., Anguita, S., 2017. Vessels fuel consumption forecast and trim optimisation: A data analytics perspective. Ocean Engineering 130, 351–370.
  • Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., Figari, M., 2016. Machine learning approaches for improving condition - based maintenance of naval propulsion plants. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 230 (1), 136–153.
  • Coraddu, A., Oneto, L., Ghio, A., Savio, S., Figari, M., Anguita, D., 2015. Machine learning for wear forecasting of naval assets for condition-based maintenance applications. In: Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), 2015 International Conference on.
  • Cristianini, N., Shawe-Taylor, J., 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
  • Geertsma, R. D., Badon Ghijben, N. A., Zwart, O. R., 2013a. Fire fighting and damage control automation: enabling future crew reduction. In: Engine as a Weapon International Symposium.
  • Geertsma, R. D., Badon Ghijben, N. A., Zwart, O. R., 2013b. Integration of combat & platform management. In: Engine as a Weapon International Symposium.
  • Geertsma, R. D., Negenborn, R. R., Visser, K., Loonstijn, M. A., Hopman, J. J., 2017. Pitch control for ships with diesel mechanical and hybrid propulsion: Modelling, validation and performance quantification. Applied Energy 206, 1609–1631.
  • Grimmelius, H. T., Stapersma, D., 2000. Control optimisation and load prediction for marine diesel engines using a mean value first principle model. Newcastle-upon-Tyne, UK, pp. 212–219.
  • Horenberg, S. C., Melaet, A. C. F., 2013. Uniting weapon and marine knowledge. In: Engine as a Weapon International Symposium.
  • Hountalas, D. T., 2000. Prediction of marine diesel engine performance under fault conditions. Vol. 20. pp. 1753– 1783.
  • IBM, 2018. User-Manual CPLEX 12.7.1. IBM Software Group.
  • Jaremkiewicz, M., 2011. Inverse heat transfer problem encountered in measurement of transient fluid temperature. Publishing of Cracow University of Technology, Cracow.
  • K¨okk¨ ul¨unk, G., Parlak, A., Erdam, H. H., 2016. Determination of performance degradation of a marine diesel engine by using curve based approach. Vol. 108. pp. 1136–1146.
  • Korczewski, Z., 2015. Exhaust gas temperature measurements in diagnostics of turbocharged marine internal combustion engines part i standard measurements. Polish Maritime Research 22 (1), 47–54.
  • Korczewski, Z., 2016. Exhaust gas temperature measurements in diagnostics of turbocharged marine internal combustion engines part ii dynamic measurements. Polish Maritime Research 23 (1), 68–76.
  • Leifsson, L., Sævarsd´ottir, H., Sigurdhsson, S., V´esteinsson, A., 2008. Grey-box modeling of an ocean vessel for operational optimization. Simulation Modelling Practice and Theory 16 (8), 923–932.
  • Majdak, M., Jaremkiewicz, M., 2016. The analysis of thermocouple time constants as a function of fluid velocity. Measurement Automation Monitoring 62.
  • Nielsen, K. V., Blanke, M., Eriksson, L., 2017. Control-oriented model of molar scavenge oxygen fraction for exhaust recirculation in large diesel engines. Vol. 139. pp. 1–9.
  • Oneto, L., 2018. Model selection and error estimation without the agonizing pain. WIREs Data Mining and Knowledge Discovery.
  • Pedersen, B. P., Larsen, J., 2009. Prediction of full-scale propulsion power using artificial neural networks. In: International Conference Computer and IT Applications in the Maritime Industries.
  • Petersen, J. P., Jacobsen, D. J., Winther, O., 2012. Statistical modelling for ship propulsion efficiency. Journal of marine science and technology 17 (1), 30–39.
  • Radonjic, A., Vukadinovic, K., 2015. Application of ensemble neural networks to prediction of towboat shaft power. Journal of Marine Science and Technology 20 (1), 64–80.
  • Roberts, I., Coney, J., Gibbs, B., 2011. Estimation of radiation losses from sheathed thermocouples. Vol. 31. pp. 2262–2270.
  • Rosasco, L., De Vito, E., Caponnetto, A., Piana, M., Verri, A., 2004. Are loss functions all the same? Neural Computation 16 (5), 1063–1076.
  • Sapra, H., Godjevac, M., Visser, K., Stapersma, D., Dijkstra, C., 2017. Experimental and simulation-based investigations of marine diesel engine performance against static back-pressure. Vol. 204. pp. 78–92.
  • Theotokatos, G., Tzelepis, V., 2015. A computational study on the performamnce and emission parameters mapping of a ship propulsion system. Vol. 229(1). pp. 58–76.
  • Tikhonov, A. N., Arsenin, V. Y., 1979. Methods for solving ill-posed problems. Nauka, Moscow.
  • Vapnik, V. N., 1998. Statistical learning theory. Wiley.
  • Wolpert, D. H., Macready, W. G., 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1), 67–82.
  • Zou, H., Hastie, T., 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2), 301–320.