Maintenance Time Prediction for Predictive Maintenance of Ship Engines

Abstract

Ships carrying large amounts of cargo and passengers are larger and slower than other modes of transportation. They are mostly foreign flagged and operate at sea far from coasts for 20 years or more, incurring more operating costs than construction costs. Therefore, an efficient maintenance system is necessary for stable, economical ship operation. Researchers are attempting to equip ships with predictive maintenance technology, which is used proactively in other modes of transportation to predict the maintenance time of machines through data monitoring and analysis. However, due to the nature of ship operation, data collection is difficult, and most studies focus on fault detection, hindering the application of predictive maintenance to ships. In this study, we developed a maintenance time prediction algorithm using the revision generator engine condition criterion (RGCCV) value and the cylinder exhaust gas temperature, as developed in a previous study for marine generator engines. And through comparison and verification using machine learning, the average mean absolute error (MAE) across all cylinders was 2.916 for the RGCCV-based method and 8.138 for the temperature-based method, demonstrating a 64% improvement. These findings establish a practical foundation for implementing predictive maintenance in ship engines by enabling more reliable and condition-based maintenance.

Keywords

• ship generator engine
• predictive maintenance
• anomalous symptoms
• maintenance time prediction
• machine learning