Machine learning methods in monitoring operating behaviour of marine two-stroke diesel engine
Abstract
The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.
Keyword : energy efficient shipping, propulsion engine, condition based maintenance, sensory data, machine learning, regression estimation, anomaly detection
How to Cite
Vorkapić, A., Radonja, R., Babić, K., & Martinčić-Ipšić, S. (2020). Machine learning methods in monitoring operating behaviour of marine two-stroke diesel engine. Transport, 35(5), 474-485. https://doi.org/10.3846/transport.2020.14038
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Breiman, L.; Cutler, A. 2004. Random Forests. Available from Internet: https://www.stat.berkeley.edu/~breiman/Random-Forests/cc_home.htm
Buhmann, M. D. 2003. Radial Basis Functions: Theory and Implementations. Cambridge University Press. 259 p. https://doi.org/10.1017/CBO9780511543241
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Coraddu, A.; Oneto, L.; Baldi, F.; Anguita, D. 2017. Vessels fuel consumption forecast and trim optimisation: A data analytics perspective, Ocean Engineering 130: 351–370. https://doi.org/10.1016/j.oceaneng.2016.11.058
Coraddu, A.; Oneto, L.; Baldi, F.; Cipollini, F.; Atlar, M.; Savio, S. 2019. Data-driven ship digital twin for estimating the speed loss caused by the marine fouling, Ocean Engineering 186: 106063. https://doi.org/10.1016/j.oceaneng.2019.05.045
Dereniowski, D.; Kubale, M. 2004. Cholesky factorization of matrices in parallel and ranking of graphs, Lecture Notes in Computer Science 3019: 985–992. https://doi.org/10.1007/978-3-540-24669-5_127
Dietterich, T. G. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization, Machine Learning 40(2): 139–157. https://doi.org/10.1023/A:1007607513941
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ISO 8754:2003. Petroleum Products – Determination of Sulfur Content – Energy-Dispersive X-Ray Fluorescence Spectrometry.
ISO 11631:1998. Measurement of Fluid Flow – Methods of Specifying Flowmeter Performance.
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Neshat, E.; Honnery, D.; Saray, R. K. 2017. Multi-zone model for diesel engine simulation based on chemical kinetics mechanism, Applied Thermal Engineering 121: 351–360. https://doi.org/10.1016/j.applthermaleng.2017.04.090
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Payri, F.; Benajes, J.; Margot, X.; Gil, A. 2004. CFD modeling of the in-cylinder flow in direct-injection diesel engines, Computers & Fluids 33(8): 995–1021. https://doi.org/10.1016/j.compfluid.2003.09.003
Rodseth, O.; Perera, L. P.; Mo, B. 2016. Big data in shipping – challenges and opportunities, in Proceedings of the 15th International Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2016), 9–11 May 2016, Lecce, Italy, 361–373.
Ra, Y.; Reitz, R. D. 2008. A reduced chemical kinetic model for IC engine combustion simulations with primary reference fuels, Combustion and Flame 155(4): 713–738. https://doi.org/10.1016/j.combustflame.2008.05.002
Reitz, R. D.; Rutland, C. J. 1995. Development and testing of diesel engine CFD models, Progress in Energy and Combustion Science 21(2): 173–196. https://doi.org/10.1016/0360-1285(95)00003-Z
Shalev-Shwartz, S.; Ben-David, S. 2014. Understanding Machine Learning: from Theory to Algorithms. Cambridge University Press. 410 p.
Shevade, S. K.; Keerthi, S. S.; Bhattacharyya, C.; Murthy, K. R. K. 2000. Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks 11(5): 1188–1193. https://doi.org/10.1109/72.870050
Smola, A. J.; Schölkopf, B. 2004. A tutorial on support vector regression, Statistics and Computing 14(3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Üstün, B.; Melssen, W. J.; Buydens, L. M. C. 2006. Facilitating the application of support vector regression by using a universal Pearson VII function based kernel, Chemometrics and Intelligent Laboratory Systems 81(1): 29–40. https://doi.org/10.1016/j.chemolab.2005.09.003
Vlahogianni, E. I. 2015. Computational intelligence and optimization for transportation big data: challenges and opportunities, Computational Methods in Applied Sciences 38: 107–128. https://doi.org/10.1007/978-3-319-18320-6_7
Vorkapić, A.; Kralj, P.; Martinović, D. 2017. The analysis of the maintenance systems of a LPG carrier’s liquefaction system main components, Pomorstvo – Scientific Journal of Maritime Research 31(1): 3–9. https://doi.org/10.31217/p.31.1.2
Westbrook, C. K.; Pitz, W. J.; Curran, H. J. 2006. Chemical kinetic modeling study of the effects of oxygenated hydrocarbons on soot emissions from diesel engines, The Journal of Physical Chemistry A 110(21): 6912–6922. https://doi.org/10.1021/jp056362g
Witten, I. H.; Frank, E.; Hall, M. A.; Pal, C. J. 2017. Data Mining: Practical Machine Learning Tools and Techniques. 4th Edition. Morgan Kaufmann. 654 p.
Wong, K. I.; Wong, P. K.; Cheung, C. S.; Vong, C. M. 2013a. Modeling and optimization of biodiesel engine performance using advanced machine learning methods, Energy 55: 519–528. https://doi.org/10.1016/j.energy.2013.03.057
Wong, K. I.; Wong, P. K.; Cheung, C. S.; Vong, C. M. 2013b. Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set, Applied Soft Computing 13(11): 4428–4441. https://doi.org/10.1016/j.asoc.2013.06.006
Benajes, J.; Novella, R.; Pastor, J. M.; Hernández-López, A.; Hasegawa, M.; Tsuji, N.; Emi, M.; Uehara, I.; Martorell, J.; Alonso, M. 2016. Optimization of the combustion system of a medium duty direct injection diesel engine by combining CFD modeling with experimental validation, Energy Conversion and Management 110: 212–229. https://doi.org/10.1016/j.enconman.2015.12.010
Breiman, L. 1994. Bagging Predictors. Technical Report No 421. Department of Statistics, University of California, Berkeley, California, US. 20 p. Available from Internet: https://www.stat.berkeley.edu/~breiman/bagging.pdf
Breiman, L. 2001. Random forests, Machine Learning 45(1): 5–32. https://doi.org/10.1023/A:1010933404324
Breiman, L.; Cutler, A. 2004. Random Forests. Available from Internet: https://www.stat.berkeley.edu/~breiman/Random-Forests/cc_home.htm
Buhmann, M. D. 2003. Radial Basis Functions: Theory and Implementations. Cambridge University Press. 259 p. https://doi.org/10.1017/CBO9780511543241
Chan, T. K.; Chin, C. S. 2016. Data analysis to predictive modeling of marine engine performance using machine learning, in 2016 IEEE Region 10 Conference (TENCON), 22–25 November 2016, Marina Bay Sands, Singapore, 2076–2080. https://doi.org/10.1109/TENCON.2016.7848391
Chapman, P.; Clinton, J.; Kerber, R.; Khabaza, T.; Reinartz, T.; Shearer, C.; Wirth, R. 2000. CRISP-DM 1.0: Step-by-Step Data Mining Guide. SPSS Inc., Chicago, IL, US. 76 p. Available from Internet: https://www.the-modeling-agency.com/crisp-dm.pdf
Cirak, B; Demirtas, S. 2014. An application of artificial neural network for predicting engine torque in a biodiesel engine, American Journal of Energy Research 2(4): 74–80. https://doi.org/10.12691/ajer-2-4-1
Coraddu, A.; Oneto, L.; Baldi, F.; Anguita, D. 2017. Vessels fuel consumption forecast and trim optimisation: A data analytics perspective, Ocean Engineering 130: 351–370. https://doi.org/10.1016/j.oceaneng.2016.11.058
Coraddu, A.; Oneto, L.; Baldi, F.; Cipollini, F.; Atlar, M.; Savio, S. 2019. Data-driven ship digital twin for estimating the speed loss caused by the marine fouling, Ocean Engineering 186: 106063. https://doi.org/10.1016/j.oceaneng.2019.05.045
Dereniowski, D.; Kubale, M. 2004. Cholesky factorization of matrices in parallel and ranking of graphs, Lecture Notes in Computer Science 3019: 985–992. https://doi.org/10.1007/978-3-540-24669-5_127
Dietterich, T. G. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization, Machine Learning 40(2): 139–157. https://doi.org/10.1023/A:1007607513941
Faber, J.; Behrends, B.; Nelissen, D. 2011. Analysis of GHG Marginal Abatement Cost Curves. Publication Number: 11.7410.21. CE Delft, Delft, The Netherlands. 58 p. Available from Internet: https://www.cedelft.eu/en/publications/download/1114
Gori, M. 2017. Machine Learning: a Constraint-Based Approach. Morgan Kaufmann. 580 p.
Gunn, S. 1998. Support Vector Machines for Classification and Regression. ISIS Technical Report. Image Speech and Intelligent Systems (ISIS) Group, University of Southampton, UK. 52 p. Available from Internet: https://svms.org/tutorials/Gunn1998.pdf
Haykin, S. 2009. Neural Networks and Learning Machines. 3rd Edition. Pearson. 936 p.
IMO. 2018. MARPOL 73/78 Annex VI Regulation 22A. International Maritime Organization (IMO).
IMO. 2011. Nitrogen Oxides (NOx) – Regulation 13. International Maritime Organization (IMO). Available from Internet: https://www.imo.org/en/OurWork/Environment/Pages/Nitrogen-oxides-(NOx)-%E2%80%93-Regulation-13.aspx
ISO 8217:2017. Petroleum Products – Fuels (Class F) – Specifications of Marine Fuels.
ISO 8754:2003. Petroleum Products – Determination of Sulfur Content – Energy-Dispersive X-Ray Fluorescence Spectrometry.
ISO 11631:1998. Measurement of Fluid Flow – Methods of Specifying Flowmeter Performance.
ISO 12185:1996. Crude Petroleum and Petroleum Products – Determination of Density – Oscillating U-Tube Method.
ISO 19030-1:2016. Ships and Marine Technology – Measurement of Changes in Hull and Propeller Performance – Part 1: General Principles.
ISO 19030-2:2016. Ships and Marine Technology – Measurement of Changes in Hull and Propeller Performance – Part 2: Default Method.
ISO 19030-3:2016. Ships and Marine Technology – Measurement of Changes in Hull and Propeller Performance – Part 3: Alternative Methods.
Kelleher, J. D.; Namee, B. M.; D’Arcy, A. 2015. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. 2nd Edition. 856 p. The MIT Press.
Kongsberg Gruppen. 2020. Shaft Power Meter, Torque and Power Measurement System for Rotating Shafts. Kongsberg Gruppen, Norway. Available from Internet: https://www.kongsberg.com/es/maritime/products/engines-engine-room-and-automation-systems/Machinery-Instrumentation/engine-monitoring-systems/shaft-power-meter-torque-and-power-measurement-system-for-rotating-shafts
MEPC. 2016. Resolution MEPC.282(70): 2016 Guidelines for the Development of a Ship Energy Efficiency Management Plan (SEEMP). Marine Environment Protection Committee (MEPC), International Maritime Organization (IMO). 19 p. Available from Internet: https://gmn.imo.org/wp-content/uploads/2017/05/MEPC-28270-2017-SEEMP-Guidelines.pdf
Mirović, M.; Miličević, M.; Obradović, I. 2018. Big data in the maritime industry, Naše More 65(1): 56–62. https://doi.org/10.17818/NM/2018/1.8
Murphy, K. P. 2006. Naive Bayes Classifiers. University of British Columbia, Vancouver, Canada. 8 p. Available from Internet: https://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall06/reading/NB.pdf
Neshat, E.; Honnery, D.; Saray, R. K. 2017. Multi-zone model for diesel engine simulation based on chemical kinetics mechanism, Applied Thermal Engineering 121: 351–360. https://doi.org/10.1016/j.applthermaleng.2017.04.090
OCIMF. 2011. GHG Emission-Mitigating Measures for Oil Tankers – Part A: Review of Reduction Potential. Oil Companies International Marine Forum (OCIMF). 25 p. Available from Internet: https://www.ocimf.org/publications/informationpapers/ghg-emission-mitigating-measures-for-oil-tankerspart-a-review-of-reduction-potential
Payri, F.; Benajes, J.; Margot, X.; Gil, A. 2004. CFD modeling of the in-cylinder flow in direct-injection diesel engines, Computers & Fluids 33(8): 995–1021. https://doi.org/10.1016/j.compfluid.2003.09.003
Rodseth, O.; Perera, L. P.; Mo, B. 2016. Big data in shipping – challenges and opportunities, in Proceedings of the 15th International Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2016), 9–11 May 2016, Lecce, Italy, 361–373.
Ra, Y.; Reitz, R. D. 2008. A reduced chemical kinetic model for IC engine combustion simulations with primary reference fuels, Combustion and Flame 155(4): 713–738. https://doi.org/10.1016/j.combustflame.2008.05.002
Reitz, R. D.; Rutland, C. J. 1995. Development and testing of diesel engine CFD models, Progress in Energy and Combustion Science 21(2): 173–196. https://doi.org/10.1016/0360-1285(95)00003-Z
Shalev-Shwartz, S.; Ben-David, S. 2014. Understanding Machine Learning: from Theory to Algorithms. Cambridge University Press. 410 p.
Shevade, S. K.; Keerthi, S. S.; Bhattacharyya, C.; Murthy, K. R. K. 2000. Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks 11(5): 1188–1193. https://doi.org/10.1109/72.870050
Smola, A. J.; Schölkopf, B. 2004. A tutorial on support vector regression, Statistics and Computing 14(3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Üstün, B.; Melssen, W. J.; Buydens, L. M. C. 2006. Facilitating the application of support vector regression by using a universal Pearson VII function based kernel, Chemometrics and Intelligent Laboratory Systems 81(1): 29–40. https://doi.org/10.1016/j.chemolab.2005.09.003
Vlahogianni, E. I. 2015. Computational intelligence and optimization for transportation big data: challenges and opportunities, Computational Methods in Applied Sciences 38: 107–128. https://doi.org/10.1007/978-3-319-18320-6_7
Vorkapić, A.; Kralj, P.; Martinović, D. 2017. The analysis of the maintenance systems of a LPG carrier’s liquefaction system main components, Pomorstvo – Scientific Journal of Maritime Research 31(1): 3–9. https://doi.org/10.31217/p.31.1.2
Westbrook, C. K.; Pitz, W. J.; Curran, H. J. 2006. Chemical kinetic modeling study of the effects of oxygenated hydrocarbons on soot emissions from diesel engines, The Journal of Physical Chemistry A 110(21): 6912–6922. https://doi.org/10.1021/jp056362g
Witten, I. H.; Frank, E.; Hall, M. A.; Pal, C. J. 2017. Data Mining: Practical Machine Learning Tools and Techniques. 4th Edition. Morgan Kaufmann. 654 p.
Wong, K. I.; Wong, P. K.; Cheung, C. S.; Vong, C. M. 2013a. Modeling and optimization of biodiesel engine performance using advanced machine learning methods, Energy 55: 519–528. https://doi.org/10.1016/j.energy.2013.03.057
Wong, K. I.; Wong, P. K.; Cheung, C. S.; Vong, C. M. 2013b. Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set, Applied Soft Computing 13(11): 4428–4441. https://doi.org/10.1016/j.asoc.2013.06.006