Digitally twin driven ship cooling pump fault monitoring system and application case

Abstract

The rise of digital twin technology has provided innovative methods for monitoring and optimizing ship cooling pumps. This paper proposes a digital twin-based framework for the status monitoring and visualization of ship cooling pumps. By establishing highly realistic physical and mathematical models and integrating actual operational data, a comprehensive virtual environment was created to simulate the operational status of ship cooling pumps. Using the random forest algorithm for data training and testing, the results showed that the root mean square error for the training set was 0.0037873, and for the test set, it was 0.008929, indicating high accuracy in predicting the status of cooling pumps. This system enables real-time monitoring, problem diagnosis, performance optimization, and decision support for cooling pumps. This study aims to leverage digital twin technology to design and apply a visualization monitoring system to enhance the intelligence of ship operation and maintenance.

Keywords

• digital twin
• ship cooling pump
• data-driven
• visualization monitoring