[2N6-GS-10] AI application

[2N6-GS-10-01] Estimating Impact of Hull Fouling Using Deep Learning

〇Takashi Matsumoto¹, Kenichi Fujiwara¹ (1. INPEX CORPORATION)

Keywords:Hull Fouling, Ship, Neural Network

The performance of a ship is greatly affected by external forces such as wind, waves and conditions of the ship such as hull fouling. Since the effects of hull fouling can be reduced by ship maintenance, conversely quantitative analysis of impact of hull fouling can be a good approach to determine the maintenance at the optimum timing and specifications. However, it is difficult to estimate the impact of hull fouling directly because multiple factors intricately affect external forces and the condition of the ship. This paper proposes a model that estimates the impact of hull fouling by using a neural network that predicts the speed of the ship from external forces such as wind and waves. The experimental results of three ships demonstrated the strong relationship between the impact of hull fouling and the effects of maintenance in a time series.