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 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.