Algorithmic support and efficiency analysis of comprehensive prescriptive maintenance for cargo ships using predictive monitoring

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.331875

Keywords:

prescriptive technical maintenance, vessel equipment, cost optimization, operational efficiency, forecasting

Abstract

The object of this study is the processes of prescriptive technical maintenance (TM) of vessel machinery and structures of cargo ships. The task addressed relates to the insufficient efficiency of conventional methods for vessel machinery TM, which leads to increased risks of failures and enhanced operating costs.

In this work, algorithmic support to a system of comprehensive prescriptive maintenance of cargo ships based on predictive monitoring methods has been developed. Data on the parameters of machinery technical condition were experimentally acquired and systemized; a comprehensive analysis of costs, risks, and reliability was carried out using the developed algorithms. The results demonstrated that applying the proposed methodology could reduce the cost of maintenance by up to 44.4%, as well as decrease the risk of malfunctions by up to 89.4%. It has been established that the total economic effect of optimizing the maintenance processes of the principal engine components equals USD 4849 per life cycle of the machinery. This confirms the feasibility and effectiveness of using comprehensive predictive monitoring in vessel TM systems.

Special feature of the results is their integrated nature, which makes it possible to simultaneously consider technical and economic aspects of TM. This is what makes it possible to avoid the shortcomings inherent in conventional regulatory systems, ensuring a higher level of operational reliability and economic efficiency of cargo ships.

The practical application of the devised methodology is possible provided that the proposed algorithms are integrated into vessel operation processes with appropriate information and analytical support, including automated data collection and continuous monitoring of machinery condition

Author Biographies

Andrii Holovan, Odesa National Maritime University

Doctor of Technical Sciences, Associate Professor

Department of Navigation and Maritime Safety

Igor Gritsuk, Vilnius Gediminas Technical University; Kherson State Maritime Academy

Doctor of Technical Sciences, Professor, Senior Research Fellow

Department of Marine Engineering

Department of Ship Technical Systems and Complexes

Valerii Verbovskyi, The Gas Institute of the National Academy of Sciences of Ukraine

PhD, Senior Researcher

Volodymyr Kalchenko, Chernihiv Polytechnic National University

Doctor of Technical Sciences, Professor

Vice-Rector for Scientific and Pedagogical Work

Yuriy Grytsuk, Ivano-Frankivsk National Technical University of Oil and Gas; National University of Ostroh Academy

PhD, Associate Professor

Department of Building Constructions, Buildings and Structures

Department of Information Technologies and Data Analytics

Oleksiy Verbovskiy, The Gas Institute of the National Academy of Sciences of Ukraine

PhD, Researcher

Serhii Dotsenko, Admiral Makarov National University of Shipbuilding

PhD, Associate Professor

Department of Power Engineering

Alla Lysykh, Admiral Makarov National University of Shipbuilding

PhD, Associate Professor

Department of Power Engineering

Roman Symonenko, State Enterprise "DergavtotransNDIproect"

Doctor of Technical Sciences, Associate Professor

Deputy Head Center for Scientific Research of Complex Transport Problems

Oleksandr Subochev, Pryazovskyi State Technical University

PhD, Associate Professor

References

  1. Podrigalo, M., Bogomolov, V., Kholodov, M., Koryak, A., Turenko, A., Kaidalov, R. et al. (2020). Energy Efficiency of Vehicles with Combined Electromechanical Drive of Driving Wheels. SAE Technical Paper Series. https://doi.org/10.4271/2020-01-2260
  2. Mateichyk, V., Saga, M., Smieszek, M., Tsiuman, M., Goridko, N., Gritsuk, I., Symonenko, R. (2020). Information and analytical system to monitor operating processes and environmental performance of vehicle propulsion systems. IOP Conference Series: Materials Science and Engineering, 776 (1), 012064. https://doi.org/10.1088/1757-899x/776/1/012064
  3. Kuric, I., Gorobchenko, O., Litikova, O., Gritsuk, I., Mateichyk, V., Bulgakov, M., Klackova, I. (2020). Research of vehicle control informative functioning capacity. IOP Conference Series: Materials Science and Engineering, 776 (1), 012036. https://doi.org/10.1088/1757-899x/776/1/012036
  4. Podrigalo, M., Klets, D., Sergiyenko, O., Gritsuk, I. V., Soloviov, O., Tarasov, Y. et al. (2018). Improvement of the Assessment Methods for the Braking Dynamics with ABS Malfunction. SAE Technical Paper Series. https://doi.org/10.4271/2018-01-1881
  5. Golovan, A., Gritsuk, I., Popeliuk, V., Sherstyuk, O., Honcharuk, I., Symonenko, R. et al. (2019). Features of Mathematical Modeling in the Problems of Determining the Power of a Turbocharged Engine According to the Characteristics of the Turbocharger. SAE International Journal of Engines, 13 (1). https://doi.org/10.4271/03-13-01-0001
  6. Golovan, A., Rudenko, S., Gritsuk, I., Shakhov, A., Vychuzhanin, V., Mateichyk, V. et al. (2018). Improving the Process of Vehicle Units Diagnosis by Applying Harmonic Analysis to the Processing of Discrete Signals. SAE Technical Paper Series. https://doi.org/10.4271/2018-01-1774
  7. Turenko, A., Podrygalo, M., Klets, D., Hatsko, V., Barun, M. (2016). A method of evaluating vehicle controllability according to the dynamic factor. Eastern-European Journal of Enterprise Technologies, 3 (7 (81)), 29–33. https://doi.org/10.15587/1729-4061.2016.72117
  8. Parsadanov, I., Marchenko, A., Tkachuk, M., Kravchenko, S., Polyvianchuk, A., Strokov, A. et al. (2020). Complex Assessment of Fuel Efficiency and Diesel Exhaust Toxicity. SAE Technical Paper Series. https://doi.org/10.4271/2020-01-2182
  9. Gritsuk, I., Pohorletskyi, D., Mateichyk, V., Symonenko, R., Tsiuman, M., Volodarets, M. et al. (2020). Improving the Processes of Thermal Preparation of an Automobile Engine with Petrol and Gas Supply Systems (Vehicle Engine with Petrol and LPG Supplying Systems). SAE Technical Paper Series. https://doi.org/10.4271/2020-01-2031
  10. Li, Y., Zou, Z., Zhang, J., He, Y., Huang, G., Li, J. (2023). Refined evaluation methods for preventive maintenance of project-level asphalt pavement based on confusion-regression model. Construction and Building Materials, 403, 133105. https://doi.org/10.1016/j.conbuildmat.2023.133105
  11. Golovan, A., Gritsuk, I., Rudenko, S., Saravas, V., Shakhov, A., Shumylo, O. (2019). Aspects of Forming the Information V2I Model of the Transport Vessel. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), 390–393. https://doi.org/10.1109/mees.2019.8896595
  12. Golovan, A., Gritsuk, I., Honcharuk, I. (2023). Principles of transport means maintenance optimization: equipment cost calculation. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 5, 77–84. https://doi.org/10.33271/nvngu/2023-5/077
  13. Karatuğ, Ç., Arslanoğlu, Y., Soares, C. G. (2023). Review of maintenance strategies for ship machinery systems. Journal of Marine Engineering & Technology, 22 (5), 233–247. https://doi.org/10.1080/20464177.2023.2180831
  14. Patil, C., Theotokatos, G., Tsitsilonis, K. (2024). Data-driven model for marine engine fault diagnosis using in-cylinder pressure signals. Journal of Marine Engineering & Technology, 24 (1), 70–82. https://doi.org/10.1080/20464177.2024.2432777
  15. Karatuğ, Ç., Ceylan, B. O., Arslanoğlu, Y. (2024). A hybrid predictive maintenance approach for ship machinery systems: a case of main engine bearings. Journal of Marine Engineering & Technology, 24 (1), 12–21. https://doi.org/10.1080/20464177.2024.2393484
  16. Liu, J., Achurra, A., Zhang, C., Bury, A., Wang, X. (2024). A long short term memory network-based, global navigation satellite system/inertial navigation system for unmanned surface vessels. Journal of Marine Engineering & Technology, 23 (5), 316–328. https://doi.org/10.1080/20464177.2024.2334029
  17. Xiao, Z., Xie, M., Wang, X., Wang, H., Fang, S., Arnáez, R. (2024). Risk assessment of emergency operations of floating storage and regasification unit. Journal of Marine Engineering & Technology, 23 (5), 357–372. https://doi.org/10.1080/20464177.2024.2364994
  18. Yuksel, O., Pamik, M., Bayraktar, M. (2025). The assessment of alternative fuel and engine power limitation utilisation in hybrid marine propulsion systems regarding energy efficiency metrics. Journal of Marine Engineering & Technology, 1–15. https://doi.org/10.1080/20464177.2025.2479319
  19. Zhang, P., Gao, Z., Cao, L., Dong, F., Zou, Y., Wang, K. et al. (2022). Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy. Machines, 10 (2), 72. https://doi.org/10.3390/machines10020072
  20. Sanchez, L., Costa, N., Couso, I. (2025). Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks. Reliability Engineering & System Safety, 261, 111135. https://doi.org/10.1016/j.ress.2025.111135
  21. Golovan, A., Gritsuk, I., Honcharuk, I. (2023). Reliable ship emergency power source: A Monte Carlo simulation approach to optimize remaining capacity measurement frequency for Lead-Acid battery maintenance. SAE International Journal of Electrified Vehicles, 13 (2). https://doi.org/10.4271/14-13-02-0009
  22. Jeon, J., Theotokatos, G. (2024). A Framework to Assure the Trustworthiness of Physical Model-Based Digital Twins for Marine Engines. Journal of Marine Science and Engineering, 12 (4), 595. https://doi.org/10.3390/jmse12040595
  23. Raptodimos, Y., Lazakis, I. (2018). Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications. Ships and Offshore Structures, 13 (6), 649–656. https://doi.org/10.1080/17445302.2018.1443694
  24. Hornyák, O. (2024). Data-Driven Engine Health Monitoring with AI. SMTS 2024, 39. https://doi.org/10.3390/engproc2024079039
  25. Visconti, P., Rausa, G., Del-Valle-Soto, C., Velázquez, R., Cafagna, D., De Fazio, R. (2024). Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review. Future Internet, 16 (11), 394. https://doi.org/10.3390/fi16110394
  26. Jasiulewicz-Kaczmarek, M., Legutko, S., Kluk, P. (2020). Maintenance 4.0 technologies – new opportunities for sustainability driven maintenance. Management and Production Engineering Review. https://doi.org/10.24425/mper.2020.133730
  27. Durlik, I., Miller, T., Kostecka, E., Tuński, T. (2024). Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications. Applied Sciences, 14 (18), 8420. https://doi.org/10.3390/app14188420
  28. Simion, D., Postolache, F., Fleacă, B., Fleacă, E. (2024). AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability. Applied Sciences, 14 (20), 9439. https://doi.org/10.3390/app14209439
  29. Yigin, B., Celik, M. (2024). A Prescriptive Model for Failure Analysis in Ship Machinery Monitoring Using Generative Adversarial Networks. Journal of Marine Science and Engineering, 12 (3), 493. https://doi.org/10.3390/jmse12030493
  30. Jimenez, V. J., Bouhmala, N., Gausdal, A. H. (2020). Developing a predictive maintenance model for vessel machinery. Journal of Ocean Engineering and Science, 5 (4), 358–386. https://doi.org/10.1016/j.joes.2020.03.003
  31. Mat Esa, M. A., Muhammad, M. (2023). Adoption of prescriptive analytics for naval vessels risk-based maintenance: A conceptual framework. Ocean Engineering, 278, 114409. https://doi.org/10.1016/j.oceaneng.2023.114409
  32. Koskina, Y., Onyshenko, S., Drozhzhyn, O., Melnyk, O. (2023). Efficiency of tramp fleet operating under the contracts of affreightment. Scientific Journal of Silesian University of Technology. Series Transport, 120, 137–149. https://doi.org/10.20858/sjsutst.2023.120.9
  33. Cheliotis, M., Lazakis, I., Theotokatos, G. (2020). Machine learning and data-driven fault detection for ship systems operations. Ocean Engineering, 216, 107968. https://doi.org/10.1016/j.oceaneng.2020.107968
  34. Onyshсhenko, S., Melnyk, O. (2021). Probabilistic Assessment Method of Hydrometeorological Conditions and their Impact on the Efficiency of Ship Operation. Journal of Engineering Science and Technology Review, 14 (6), 132–136. https://doi.org/10.25103/jestr.146.15
  35. Sagin, S. V., Semenov, O. V. (2016). Motor Oil Viscosity Stratification in Friction Units of Marine Diesel Motors. American Journal of Applied Sciences, 13 (2), 200–208. https://doi.org/10.3844/ajassp.2016.200.208
  36. Sagin, S. V., Solodovnikov, V. G. (2017). Estimation of operational properties of lubricant coolant liquids by optical methods. International Journal of Applied Engineering Research, 12 (19), 8380–8391. Available at: https://www.ripublication.com/ijaer17/ijaerv12n19_51.pdf
  37. Tong, S., Yanqiao, C., Yuan, Z. (2020). Fault prediction of marine diesel engine based on time series and support vector machine. 2020 International Conference on Intelligent Design (ICID), 75–81. https://doi.org/10.1109/icid52250.2020.00023
  38. Zhan, Y., Shi, Z., Liu, M. (2007). The Application of Support Vector Machines (SVM) to Fault Diagnosis of Marine Main Engine Cylinder Cover. IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, 3018–3022. https://doi.org/10.1109/iecon.2007.4459891
  39. Vychuzhanin, V., Rudnichenko, N., Shybaiev, D., Gritsuk, I., Boyko, V., Shybaieva, N. et al. (2018). Cognitive Model of the Internal Combustion Engine. SAE Technical Paper Series. https://doi.org/10.4271/2018-01-1738
  40. Melnyk, O., Onishchenko, O., Onyshchenko, S. (2023). Renewable Energy Concept Development and Application in Shipping Industry. Lex Portus, 9 (6). https://doi.org/10.26886/2524-101x.9.6.2023.2
  41. Golovan, A., Mateichyk, V., Gritsuk, I., Lavrov, A., Smieszek, M., Honcharuk, I., Volska, O. (2024). Enhancing Information Exchange in Ship Maintenance through Digital Twins and IoT: A Comprehensive Framework. Computers, 13 (10), 261. https://doi.org/10.3390/computers13100261
  42. Volyanskaya, Y., Volyanskiy, S., Onishchenko, O., Nykul, S. (2018). Analysis of possibilities for improving energy indicators of induction electric motors for propulsion complexes of autonomous floating vehicles. Eastern-European Journal of Enterprise Technologies, 2 (8 (92)), 25–32. https://doi.org/10.15587/1729-4061.2018.126144
  43. Vychuzhanin, V. V., Rudnichenko, N. R., Sagova, Z., Smieszek, M., Cherniavskyi, V. V., Golovan, A. I., Volodarets, M. V. (2020). Analysis and structuring diagnostic large volume data of technical condition of complex equipment in transport. IOP Conference Series: Materials Science and Engineering, 776 (1), 012049. https://doi.org/10.1088/1757-899x/776/1/012049
  44. Senova, A., Tobisova, A., Rozenberg, R. (2023). New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method. Sustainability, 15 (2), 1006. https://doi.org/10.3390/su15021006
Algorithmic support and efficiency analysis of comprehensive prescriptive maintenance for cargo ships using predictive monitoring

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Published

2025-06-30

How to Cite

Holovan, A., Gritsuk, I., Verbovskyi, V., Kalchenko, V., Grytsuk, Y., Verbovskiy, O., Dotsenko, S., Lysykh, A., Symonenko, R., & Subochev, O. (2025). Algorithmic support and efficiency analysis of comprehensive prescriptive maintenance for cargo ships using predictive monitoring. Eastern-European Journal of Enterprise Technologies, 3(3 (135), 13–26. https://doi.org/10.15587/1729-4061.2025.331875

Issue

Vol. 3 No. 3 (135) (2025): Control processes

Section

Control processes

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Copyright (c) 2025 Andrii Holovan, Igor Gritsuk, Valerii Verbovskyi, Volodymyr Kalchenko, Yuriy Grytsuk, Oleksiy Verbovskiy, Serhii Dotsenko, Alla Lysykh, Roman Symonenko, Oleksandr Subochev

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