Andrii Golovan
@onmu.org.ua
Department of Navigation and Maritime Safety
Odesa National Maritime University
RESEARCH, TEACHING, or OTHER INTERESTS
Transportation, Control and Systems Engineering, Water Science and Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Andrii Golovan, Igor Gritsuk, and Iryna Honcharuk
SAE International
<div>The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance. The methodology employed in this study includes outlining the theoretical development of a fully automated condition monitoring system and describing data cleansing steps to account for environmental effects on system performance. A Monte Carlo simulation is used to evaluate the sensitivity of the remaining useful life prediction to varying measurement frequencies, prediction models, and parameter settings, leading to an estimate of the optimal measurement frequency for the system. The results show that a certain minimum measurement frequency is required to achieve the target prediction accuracy while balancing cost-efficiency and operational stability. Reliable failure prediction with negligible changes in prognostic accuracy can be achieved by performing useful remaining lead-acid battery capacity measurements twice a day or every 5 ship voyage cycles with the underlying utilization.</div>
A Golovan, I Gritsuk, and I Honcharuk
Dnipro University of Technology
Purpose. Justification of principles and methodology for effective calculation of the equipment costs and optimization of transport means maintenance. Methodology. The results of the presented scientific research were obtained using general and special methods of cognition: abstract and logical analysis, systematization and combination, method of theoretical generalization, method of dialectical cognition, deduction and induction, and statistical analysis. This paper analyzes the relationship between the probability of failure prevention by the maintenance system and the associated costs. The research investigates how the variation in the technical condition change rate influences the length of the operation cycle and the rate of its decline. The study’s outcomes are analyzed, including the formation of points of minimum unit costs, the effect of spare parts’ cost, and the practical importance of the conclusions drawn. Findings. This paper outlines the economic methodology for determining the specific expenses of maintaining means of transport. The methodology considers the distribution of expenses for spare parts, labor, and other components. Using this methodology, it is possible to estimate the total costs of maintenance and make informed decisions about the efficient use of resources. It has been determined that the cost of spare parts impacts the efficiency of the maintenance system. Therefore, it is imperative to balance the cost for spare parts and safety, while considering the probability of failure. The method outlined in this work is versatile, which allows its adaptation and application to the specialized road transport. Originality. The paper further develops the methodological approach to calculating equipment costs for transport maintenance, which is used to improve service efficiency and reduce expenses. The approach enables a comprehensive evaluation of the outcomes of enhancing failure prevention probability through the maintenance system. It also aids in managing unused parts’ resources, particularly during short operating cycles. Practical value. The study’s findings can optimize the maintenance system, increase operational efficiency, and enhance the safety and reliability of means of transport, while reducing the costs associated with spare parts, labor, and other maintenance components. This approach aids in conserving resources, reducing operating costs, and is crucial for the financial stability and profitability of management companies.
V V Vychuzhanin, N R Rudnichenko, Z Sagova, M Smieszek, V V Cherniavskyi, A I Golovan, and M V Volodarets
IOP Publishing
Abstract The paper presents the results of the classification analysis model for structuring of processed large volumes of heterogeneous diagnostic data about the technical state of complex equipment in transport development and research. Concept for the description and structuring of big data is proposed based on the formation of a metadata scheme using logical breakdown of all technical diagnostic data on the output variable - the technical condition of complex technical equipment in transport. A functional assessment of the technical condition complex technical system’s elements in transport is developed based on the application of methods for assessing structural and functional risks of failures. The article presents the results of assessing the accuracy of the input data sets classification using created decision trees models to effectively structuring and presenting the data in order to ensure that the procedures for their further analysis are performed. As a result of using the developed simulation model of structuring and presenting large heterogeneous diagnostic data volumes about the state of complex technical equipment in transport the time costs were reduced and the efficiency of analytical operations to study data for solving diagnostic problems and predicting complex system’s technical condition was improved.
Andrii Golovan, Igor Gritsuk, Maksym Kurtsev, Oksana Ischuka, and Roman Vrublevskyi
Springer International Publishing
Andrii Golovan, Igor Gritsuk, Vadym Popeliuk, Olga Sherstyuk, Iryna Honcharuk, Roman Symonenko, Viktoriya Saravas, Mykyta Volodarets, Maksym Ahieiev, Dmytro Pohorletskyi,et al.
SAE International
Andrii Golovan, Igor Gritsuk, Sergey Rudenko, Viktoriya Saravas, Anatoliy Shakhov, and Oleksandr Shumylo
IEEE
The article describes the aspects of forming the information V2I model, the process of preparation, monitoring and assessment of the technical state of the transport vessel power plant under operating conditions with the possibility of predicting its technical state. The authors developed an informational V2I model, which is characterized by the vibrational field of the transport vessel, means of monitoring of the technical state parameters and infrastructure components for monitoring any transport vessel. The main idea is to implement information monitoring system designed to ensure the efficient operation of the vehicle in non-stationary operating conditions, characterized by the lack of quality data transmission networks.
Mykyta Volodarets, Igor Gritsuk, Nataliia Chygyryk, Evgen Belousov, Andrii Golovan, Olena Volska, Vitalii Hlushchenko PhD, Dmytro Pohorletskyi, and Olga Volodarets
SAE International
Mikhail Podrigalo, Dmytro Klets, Oleg Sergiyenko, Igor V. Gritsuk, Oleh Soloviov, Yuriy Tarasov, Maksym Baitsur, Nickolay Bulgakov, Vasyl Hatsko, Andrii Golovan,et al.
SAE International
Vladimir Vychuzhanin, Nickolay Rudnichenko, Denys Shybaiev, Igor Gritsuk, Victor Boyko, Natalia Shybaieva, Andrii Golovan, Victor Zaharchuk, Ernest Rabinovich, Volodymyr Savchuk,et al.
SAE International
Ernest Rabinovich, Igor V. Gritsuk, Vladimir Zuiev, Evgeny Zenkin E.Y., Andrii Golovan, Yuriy Zybtsev, Vladimir Volkov, Juraj Gerlici, Kateryna Kravchenko, Olena Volska,et al.
SAE International
Victor Zaharchuk, Igor V. Gritsuk, Oleg Zaharchuk, Andrii Golovan, Sergey Korobka, Larisa Pylypiuk, and Nickolay Rudnichenko
SAE International
Andrii Golovan, Sergey Rudenko, Igor Gritsuk, Anatoliy Shakhov, Vladimir Vychuzhanin, Vasyl Mateichyk, Olga Kononova, Ivan Kuric, Milan Saga, and Evgeny Zenkin E.Y.
SAE International
Igor V. Gritsuk, Evgeny Zenkin E.Y., Nickolay Bulgakov, Andrii Golovan, Ivan Kuric, Vasyl Mateichyk, Milan Saga, Vladimir Vychuzhanin, Roman Symonenko, Ernest Rabinovich,et al.
SAE International