Algorithmic support and efficiency analysis of comprehensive prescriptive maintenance for cargo ships using predictive monitoring - Holovan, Andrii; Gritsuk, Igor; Verbovskyi, Valerii; Kalchenko, Volodymyr; Grytsuk, Yuriy; Verbovskiy, Oleksiy; Dotsenko, Serhii; Lysykh, Alla; Symonenko, Roman; Subochev, Oleksandr (Eastern-European Journal of Enterprise Technologies, 2025)
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