Hull Deformation Estimation Based on Neural Network

Abstract

Hull deformation is an important factor affecting the consistency of ship attitude. Hull deformation measurement methods, such as optical measurement method, inertial matching measurement method, etc., can accurately measure the hull deformation. However, these measurement methods require the installation of deformation measurement equipment at the location where the deformation is measured, which limits the deformation measurement method to be used on large ships with numerous onboard equipment. In this paper, a ship deformation estimation technology based on neural network is proposed, analyzes the ship deformation transfer law, builds the ship deformation estimation neural network model, and estimates the deformation information of adjacent positions through the deformation information of the known position, so as to obtain the whole ship deformation information, and the attitude of the whole ship can be achieved. Finally, the experiment is carried out on a ship model, the relative deformation data of different positions and multiple directions are collected, the neural network model is trained, and the model is verified with the test data. The results prove the feasibility of estimating ship deformation transfer based on neural network.