Reliable Ship Emergency Power Source: A Monte Carlo Simulation Approach to Optimize Remaining Capacity Measurement Frequency for Lead-Acid Battery Maintenance

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.