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ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Prof. Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
Multiparameter Approximation Model of Temperature Conditions of Marine Diesel Generator Sets, Based on Markov Chain Monte Carlo
1 National University “Odessa Maritime Academy”, Odessa, Ukraine
ABSTRACT: In the article we propose a multi-parameter approximation model, based on Markov chain Monte Carlo, which describes the relationship between the temperature regime, operating conditions and electromechanical parameters of marine diesel generator sets. The approximation model is constructed on the basis of the analysis of experimental data of the exhaust gases temperature of marine diesel generator sets in their long-term operation. As a statistical model of random processes of temperature deviations from the approximation model, a Markov process model is proposed that takes into account the possible correlation of the initial data. Since the measuring channels of modern diagnostic systems are digital, due to discretization in time and level, the studied processes form a Markov chain, which makes it possible to establish the important features of such processes. The use of approximation models ensures the stationarity conditions and the correctness of the proposed Markov model in the conditions of multi-mode operation of marine diesel generator sets. The proposed multi-parameter approximation model, based on Markov chain Monte Carlo, allows you to take into account random perturbations that lead to a random change in the output coordinates of the diagnostic object. The proposed improvement of the model makes it possible to ensure its adequacy to real processes of changing the parameters of the temperature regimes of marine diesel generator sets. The proposed multi-parameter approximation model, based on Markov chain Monte Carlo, can be used in the systems of technical diagnostics of marine diesel generator sets in order to increase the reliability of diagnostic conclusions.
REFERENCES
Hvozdeva, I., Myrhorod, V., Derenh, Y. The Method of Trend Analysis of Parameters Time Series of Gas-turbine Engine State. In AMiTaNS’17, AIP Conf. Proc. edited by M. D. Todorov. American Institute of Physics, Melville, NY, 2017, vol. 1895, pp. 030002-1-030002-9. DOI: 10.1063/1.5007361. - doi:10.1063/1.5007361
Myrhorod, V., Hvozdeva, I., Derenh, Y. Two-dimensional trend analysis of time series of complex technical objects diagnostic parameters. 11th Interna-tional Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’19, AIP Conference Proceedings, 060013, 2019, vol. 2164, no. 1, pp. 040011-1-040011-12. DOI: 10.1063/1.5130815. - doi:10.1063/1.5130815
Myrhorod, V. Multi-parameter Diagnostic Model of the Technical Conditions Changes of Ship Diesel Generator Sets [Text] / V. Myrhorod, I. Hvozdeva, V. Budashko // 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP), Kremenchuk, 21-25 Sept. 2020, Ukraine: IEEE. Pp. 1-5. DOI: 10.1109/PAEP49887.2020.9240905. - doi:10.1109/PAEP49887.2020.9240905
Breikin, T., Arkov , V., Kulikov G. Application of Markov chains to identification of gas turbine engine dynamic models, Periodicals International Journal of Systems Science, 2006, Vol. 37, No. 3, Issue 320, pp. 197–205. doi.org/10.1080/00207720600566065 - doi:10.1080/00207720600566065
Zhengmao Ye, Habib Mohamadian. Simple Engine Exhaust Temperature Modeling and System Identification Based on Markov Chain Monte Carlo, Applied Mechanics and Materials, Vol. 598 (2014) pp. 224-228. doi:10.4028/www.scientific.net/AMM.598.224 - doi:10.4028/www.scientific.net/AMM.598.224
Wartsila 2 stroke engines Manual “Operator flexView”, Switzerland, 2008, pp. 152–153.
Wartsila RT-flex82C Operating manual “Marine”, Rev 2.3.1, 2009, pp. 42–43.
I. Hvozdeva and V. Demirov “Trend control methods in the modern ships power plants diagnostic systems”, Visnyk of Kherson National Technical University. Kherson, vol. 3(58), 2016, pp. 191–194.
Kongsberg Norcontrol marine automation systems. Norway, 2005.
M. Kendall and A. Stuart “The advanced theory of statistics”. New York: Hafner, 1979, vol. 2.
O.D. Anderson “Time series analysis and forecasting”. London: Butterworths, 1976.
G.E.P. Box and G.M. Jenkins, “Time series analysis: Forecasting and control”. San Francisco: Holden Day, 1976.
D.C. Montgomery, L.A. Johnson and J. S. Gardiner, “Forecasting and time series analysis”. New York: McGraw-Hill, 1990.
R.H. Shumway, “Applied statistical time series analysis”. New York: Prentice Hall, 1988.
W.W. Wei, “Time series analysis: Univariate and multivariate methods”. New York: Addison-Wesley, 1989.
C. Rethfeldt, A. U. Schubert, R. Damerius, M. Kurowski, T. Jeinsch, System Approach for Highly Automated Manoeuvring with Research Vessel DENEB, IFAC-PapersOnLine, 2021, vol. 54, i. 16, pp. 153-160. ISSN 2405-8963. Doi: - doi:10.1016/j.ifacol.2021.10.087
P. Dvulit, S. Savchuk, I. Sosonka, Accuracy estimation of site coordinates derived from GNSS-observations by non-classical error theory of measurements, Geodesy and Geodynamics, 2021, vol. 12, i. 5, pp. 347-355. ISSN 1674-9847. Doi: - doi:10.1016/j.geog.2021.07.005
V. V. Budashko, Design of the three-level multicriterial strategy of hybrid marine power plant control for a combined propulsion complex, Electrical engineering & electromechanics, 2017, vol. 2, pp. 62-72. Doi:10.20998/2074-272X.2017.2.10. - doi:10.20998/2074-272X.2017.2.10
T. Fonseca, K. Lagdami, J.-U. Schröder-Hinrichs, Assessing innovation in transport: An application of the Technology Adoption (TechAdo) model to Maritime Autonomous Surface Ships (MASS), Transport Policy, 2021, vol. 114, pp. 182-195. ISSN 0967-070X. Doi: - doi:10.1016/j.tranpol.2021.09.005
M. P. Barde, P. J. Barde, What to use to express the variability of data: Standard deviation or standard error of mean?, Perspect Clin, 2012, res. 3 (3), pp. 113-116. Doi: https://dx.doi.org/10.4103%2F2229-3485.100662. - doi:10.4103/2229-3485.100662
M. Krčum, Ž. Lazarević, I. Kuzmanić, Shipboard Monitoring and Control System, IFAC Proceedings Volumes, 1997, vol. 30, i. 22, pp. 165-169. ISSN 1474-6670. Doi: - doi:10.1016/S1474-6670(17)46508-6
M. Zhang, J. Montewka, T. Manderbacka, P. Kujala, S. Hirdaris, A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions, Reliability Engineering & System Safety, 2021, vol. 213, 107674. ISSN 0951-8320. Doi: - doi:10.1016/j.ress.2021.107674
V. Budashko, Thrusters physical model formalization with regard to situational and identification factors of motion modes, 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, 12-13 June 2020, Turkey: IEEE, pp. 1-6. Doi: - doi:10.1109/ICECCE49384.2020.9179301
V. Budashko, V. Shevchenko, The synthesis of control system to synchronize ship generator assemblies, Eastern-European Journal of Enterprise Technologies, 2021, vol. 1, i. 2(109), pp. 45-63. ISSN 1729-3774. Doi: 10.15587/1729-4061.2021.225517. - doi:10.15587/1729-4061.2021.225517
V. Budashko, V. Shevchenko, Solving a task of coordinated control over a ship automated electric power system under a changing load, Eastern-European Journal of Enterprise Technologies, 2021, vol. 2, i. 2(110), pp. 54-70. ISSN 1729-3774. Doi: 10.15587/1729-4061.2021.229033. - doi:10.15587/1729-4061.2021.229033
R. Tang, Q. An, F. Xu, X. Zhang, X. Li, J. Lai, Z. Dong, Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method, Energy, 2020, v. 211, 119077. ISSN 0360-5442. - doi:10.1016/j.energy.2020.119077
Kühnel, N. Implementing an adaptive traffic signal control algorithm in an agent-based transport simulation [Text] / N. Kühnel, T. Thunig, K. Nagel // Procedia Computer Science. – 2018. – V. 130. – P. 894-899. ISSN 1877-0509. Doi: 10.1016/j.procs.2018.04.086. - doi:10.1016/j.procs.2018.04.086
V. Budashko, V. Golikov, Theoretical-applied aspects of the composition of egression models for combined propulsion complexes based on data of experimental research [Text], Eastern-European Journal of Enterprise Technologies, 2017, vol. 4, i. 3(88), pp. 11-20. Doi:10.15587/1729-4061.2017.107244. - doi:10.15587/1729-4061.2017.107244
Yang, C. Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle [Text] / C. Yang, S. Du, L. Li, S. You, Y. Yang, Y. Zhao // Applied Energy. – 2017. – V. 203. – P. 883-896. ISSN 0306-2619. Doi: 10.1016/j.apenergy.2017.06.106. - doi:10.1016/j.apenergy.2017.06.106
Citation note:
Myrhorod V., Hvozdeva I., Budashko V.: Multiparameter Approximation Model of Temperature Conditions of Marine Diesel Generator Sets, Based on Markov Chain Monte Carlo. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 16, No. 4, doi:10.12716/1001.16.04.20, pp. 779-784, 2022
Authors in other databases:
Volodymyr Myrhorod: Scopus icon57191840060
Iryna Hvozdeva: Scopus icon57191838593 Scholar iconhfy0Yn8AAAAJ

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