PATTERN MODELING FOR DAMAGE DETECTION AND DOWNTIME PREDICTION IN NEW MERCHANT SHIP MACHINERY SYSTEMS AS BASIS FOR MAINTENANCE OPTIMIZATION
Keywords:
Machinery Downtime, Maintenance Optimization, Merchant Ships, Pattern Modeling, Predictive MaintenanceAbstract
This research investigates pattern modeling approaches for implementing and detecting damage and downtime in new merchant ship machinery systems to establish foundations for maintenance optimization. Modern merchant vessels incorporate sophisticated propulsion and auxiliary machinery requiring proactive maintenance strategies to minimize costly unplanned downtime and maximize operational availability. Through qualitative analysis involving fleet managers, chief engineers, maintenance planners, and data analytics specialists, this study examines how operational data patterns can inform predictive maintenance frameworks, optimize maintenance scheduling, and reduce machinery failures. Results demonstrate that systematic pattern recognition analyzing vibration signatures, temperature trends, oil analysis parameters, and performance indicators can predict machinery failures 5-7 days in advance with 78-85% accuracy, enabling preventive interventions before critical breakdowns occur. Key implementation challenges include data quality and availability, analytical expertise requirements, integration with existing maintenance management systems, and organizational culture transitions from time-based to condition-based maintenance philosophies. Findings reveal that pattern modeling-based predictive maintenance can reduce unplanned downtime by 40-60%, extend machinery lifespan by 20-30%, and decrease maintenance costs by 15-25% while improving operational reliability. This research contributes to maritime maintenance literature by providing empirical frameworks for data-driven maintenance optimization applicable to modern merchant vessel operations
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Copyright (c) 2026 Ardiansyah Ardiansyah, Aldo Deviano, Yok Suprobo, M. Ely Ridwan, Natanael Suranta (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
