AI-DRIVEN CONDITION MONITORING OF MARINE ENGINE SHAFT VIBRATION
Abstract
This project presents the development of an AI-driven condition monitoring system for assessing the vibration signatures of marine engine shafting systems. The primary aim is to model the performance characteristics of the shafting system and leverage advanced machine learning algorithms to analyze real-time data and predict system conditions. By integrating machine learning with historical data from existing literature, the system can identify potential faults, inefficiencies, and performance trends. The proposed solution is compared with established theoretical frameworks to evaluate its accuracy and efficiency. The scope of this work is focused on simulation-based designs and predictive modeling, applicable to a wide range of maritime applications. However, it does not delve into vessel-specific design and implementation details. This research provides a foundation for future AI applications in maritime condition monitoring.
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