Large ships adopt a central fresh water-cooling system that indirectly cools waste heat with seawater to discharge the ship′s waste heat out of the ship. Such a central fresh water-cooling system is essential for future electric powered ships. Since 2010, shipping companies have attempted to save energy by applying variable-speed cooling pumps to the central FW cooling system, but due to the minimum-required discharge pressure of the pump, they have applied the existing 3-way valve system alongside. However, since the control systems of the variable-speed cooling pump and the 3-way valve are controlled by the same output variable, the two control systems collide during operation. Therefore, for efficient energy-saving control, it is important to accurately model the central fresh water-cooling system and find the optimal control method on this basis. In this study, a ship’s central cooling system was mathematically modeled and verified by comparing it with the actual ship′s operation data. A control solution method to effectively save energy for the central cooling system was proposed

As the development of autonomous ships is underway in the maritime industry, the automation of ship spare part management has become an important issue. However, there has been little development of dedicated devices or applications for ships. This study aims to develop a Raspberry Pi-based embedded application that identifies the type and quantity of spare parts using a transfer learning model and image processing algorithm suitable for ship spare part recognition. A newly improved image processing algorithm was used to select a transfer learning model that balances accuracy and training speed through training and validation on a real spare parts dataset, achieving a prediction accuracy of 98.2% and a training time of 158 s. The experimental device utilizing this model used a camera to identify the type and quantity of spare parts on an actual ship. It displayed the spare parts list on a remotely connected computer. The ASSM (Automated Ship Spare-Part Management) device utilizing image processing and transfer learning is a new technology that successfully automates spare part management.