Sea state estimation using monitoring data by convolutional neural network (CNN)
Sea state estimation using monitoring data by convolutional neural network (CNN)
复制标题
利用卷积神经网络（CNN）监测数据进行海况估计
DOI：
10.1007/s00773-020-00785-8
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发表时间：
2021
影响因子：
2.6
通讯作者：
Xi Chen,
中科院分区：
文献类型：
--
作者：
Toshiki Kawai;Yasumi Kawamura;Tetsuo Okada;Taiga Mitsuyuki;Xi Chen,
近年来，集装箱船的大型化，对船体结构安全性的要求越来越高。为了评估结构在运行中的安全性，需要掌握所遇到的海况。本研究的目的是使用机器学习根据远洋14，000 TEU集装箱船的测量数据来估计遇到的海况。在本文中，作为研究的第一步，相当数量的虚拟海况数据和相应的船舶运动和结构响应数据的准备。利用这些数据开发了一个卷积神经网络（CNN），以估计基于船体响应的遭遇海浪的方向谱。神经网络的输入参数包括船舶运动谱和结构反应谱。CNN的输出包括Ochi-Hubble谱的海况参数，具体而言，有效波高，模态波频率，平均波向，峰度和波能方向分布的集中度。从性能检验中发现，所开发的CNN能够准确地估计海况参数，尽管当船体响应较低时，精度水平降低。然而，当船体响应较低时，精度的降低对估计的海况的结构响应的评估具有微弱的影响。
In recent years, the size of container ships has become larger, thus requiring a more evident assurance of the hull structural safety. In order to evaluate the structural safety in operation, it is necessary to grasp the encountered sea state. The aim of this study is to estimate the encountered sea state using machine learning from measurement data of ocean-going 14,000TEU container ships. In this paper, as a first step in the study, considerable amounts of virtual sea state data and corresponding ship motion and structural response data are prepared. A convolutional neural network (CNN) is developed using these data to estimate the directional wave spectrum of encountered sea based on the hull responses. The input parameters of the formulated CNN include the spectral values of ship motion and structural response spectrum. The output of the CNN includes the sea state parameters of the Ochi-Hubble spectrum, specifically, significant wave height, modal wave frequency, mean wave direction, kurtosis, and concentration of wave energy directional distribution. It is found from the performance examination that the developed CNN is capable of accurately estimating the sea state parameters, although the level of accuracy decreases when the hull response is low. However, the decrease in accuracy when the hull response is low has a weak influence on the evaluation of the structural response to the estimated sea state.