

Some issues of increasing the energy efficiency of ships by improving navigation methods
This section is dedicated to the systematization of digital solutions in the field of ship maintenance, with a focus on enhancing the reliability of ship equipment, reducing costs, and improving operational performance. The analysis of previous studies and publications is used to identify potential challenges and demonstrate how the application of digital strategies can effectively address them. The section explores modern approaches to implementing digital strategies in the context of systematic maintenance of cargo vessels, outlines the main challenges related to the inefficiencies of traditional maintenance practices, and substantiates the necessity of adopting digital strategies to achieve sustainable shipping objectives.
The emphasis is placed on the integration of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), digital twins, and Big Data to ensure the reliable operation of fleet technical systems. It is justified that the use of digital models not only enhances the safety and efficiency of maintenance processes but also enables significant reductions in fuel consumption through optimized engine load management, early fault detection, and predictive maintenance planning.
A digital strategy development approach is proposed, based on the principles of energy-efficient lifecycle management of ship systems. The potential of virtualized maintenance is analyzed as a means of minimizing resource consumption, human effort, and environmental impact. Examples are provided of digital platforms being implemented to monitor the technical condition of vessels in real time, which allows for increased flexibility in decision-making and reduced downtime. The section also discusses and substantiates optimal parameters for a ship maintenance system based on the use of digital strategies.
Doctor of Technical Sciences, Associate Professor
Department of Navigation and Maritime Safety
https://orcid.org/0000-0001-6589-4381
Golovan, A. I. (2023). Formation of digital strategies for solving problems of increasing the efficiency of cargo ship maintenance systems. Reporter of the Priazovskyi State Technical University. Section: Technical Sciences, 46, 149–158. LOCKSS. https://doi.org/10.31498/2225-6733.46.2023.288184
Liu, W., Shi, C., Liu, Z., Shi, Y. (Eds.) (2025). Maritime Infrastructure for Energy Management and Emission Reduction Using Digital Transformation. Studies in Infrastructure and Control. Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4438-4
Qi, Q., Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/access.2018.2793265
Huang, L., Pena, B., Liu, Y., Anderlini, E. (2022). Machine learning in sustainable ship design and operation: A review. Ocean Engineering, 266, 112907. https://doi.org/10.1016/j.oceaneng.2022.112907
Abuella, M., Fanaee, H., Nowaczyk, S., Johansson, S., Faghani, E. (2025). Time-series analysis approach for improving energy efficiency of fixed-route passenger vessel in short-sea shipping. Ocean Engineering, 334, 121555. https://doi.org/10.1016/j.oceaneng.2025.121555
Lee, J., Davari, H., Singh, J., Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
Velasco-Gallego, C., Navas De Maya, B., Matutano Molina, C., Lazakis, I., Cubo Mateo, N. (2023). Recent advancements in data-driven methodologies for the fault diagnosis and prognosis of marine systems: A systematic review. Ocean Engineering, 284, 115277. https://doi.org/10.1016/j.oceaneng.2023.115277
Zhyrov, G. (2020). Analysis of problem optimization of parameters maintenance process according to state with constant periodicity of control. International Journal of Emerging Trends in Engineering Research, 8 (6), 2606–2611. https://doi.org/10.30534/ijeter/2020/63862020
Verma, A. K., Srividya, A., Rana, A., Khattri, S. K. (2012). Optimization of maintenance scheduling of ship borne machinery for improved reliability and reduced cost. International Journal of Reliability, Quality and Safety Engineering, 19 (3), 1250014. https://doi.org/10.1142/s0218539312500143
Haider, R., Kakar, A. M., Khattak, S. B., Rehman, S. U., Maqsood, S., Ullah, M. et al. (2015). Development of optimized maintenance system for vehicle fleet. Journal of Engineering and Applied Sciences, University of Engineering and Technology, Peshawar, 34 (2), 21–28. Available at: https://www.researchgate.net/publication/297136711_DEVELOPMENT_OF_OPTIMIZED_MAINTENANCE_SYSTEM_FOR_VEHICLE_FLEET
Emovon, I., Norman, R. A., Murphy, A. J. (2015). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29 (3), 519–531. https://doi.org/10.1007/s10845-015-1133-6
Golovan, A., Honcharuk, I., Deli, O., Kostenko, O., Nykyforov, Y. (2021). System of Water Vehicle Power Plant Remote Condition Monitoring. IOP Conference Series: Materials Science and Engineering, 1199 (1), 012049. https://doi.org/10.1088/1757-899x/1199/1/012049
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
Kim, T., Song, J. (2018). Generalized Reliability Importance Measure (GRIM) using Gaussian mixture. Reliability Engineering & System Safety, 173, 105–115. https://doi.org/10.1016/j.ress.2018.01.005

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.