Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model
This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Wei...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/4/798 |
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| author | Damir Budimir Dario Medić Vlatka Ružić Mateja Kulej |
| author_facet | Damir Budimir Dario Medić Vlatka Ružić Mateja Kulej |
| author_sort | Damir Budimir |
| collection | DOAJ |
| description | This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms. |
| format | Article |
| id | doaj-art-3894786269a44645997faf3671e47864 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-3894786269a44645997faf3671e478642025-08-20T02:17:59ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113479810.3390/jmse13040798Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA ModelDamir Budimir0Dario Medić1Vlatka Ružić2Mateja Kulej3Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, CroatiaUniversity of Applied Sciences “Nikola Tesla”, Bana Ivana Karlovića 16, 53000 Gospić, CroatiaFaculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, CroatiaThis study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms.https://www.mdpi.com/2077-1312/13/4/798technical logisticsmaintenance optimizationWeibull analysisMarkov chainsDEA (data envelopment analysis)predictive maintenance |
| spellingShingle | Damir Budimir Dario Medić Vlatka Ružić Mateja Kulej Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model Journal of Marine Science and Engineering technical logistics maintenance optimization Weibull analysis Markov chains DEA (data envelopment analysis) predictive maintenance |
| title | Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model |
| title_full | Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model |
| title_fullStr | Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model |
| title_full_unstemmed | Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model |
| title_short | Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model |
| title_sort | integrated approach to marine engine maintenance optimization weibull analysis markov chains and dea model |
| topic | technical logistics maintenance optimization Weibull analysis Markov chains DEA (data envelopment analysis) predictive maintenance |
| url | https://www.mdpi.com/2077-1312/13/4/798 |
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