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首页> 外文期刊>Ocean Engineering >Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations
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Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations

机译：船舶运动建模的数据驱动的不确定性和敏感性分析

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摘要

To build a compact data-driven ship motion model for offshore operations that require high control safety, it is necessary to select the most influential parameters and to analyze the uncertainty of the input parameters. This paper proposes a framework of uncertainty and sensitivity analysis for ship motion data. The framework consists of four components: data cleaning, surrogate model, sensitivity and uncertainty analysis, and results visualization. Data cleaning focuses on the removal of noise, and necessary transformation for the easy analysis. An artificial neural network (ANN) based surrogate model is constructed on the basis of cleaned ship motion data. The sensitivity and uncertainty analysis would be performed on the sample or weights which the ANN based surrogate model generated. The result of the sensitivity and uncertainty analysis can be beneficial to the optimization of data-driven ship motion models. Three distinctive sensitivity analysis (SA) methods (Garson/Morris/Sobol), and PDF-based and CDF-based uncertainty methods are investigated in two types of ship motion datasets with and without environmental factors. The experimental results also demonstrate the proposed framework can be applied to estimate the sensitivity and uncertainty in different datasets.
机译：为需要高控制安全性的海上操作构建紧凑的数据驱动船舶运动模型，有必要选择最有影响力的参数并分析输入参数的不确定性。本文提出了船舶运动数据的不确定性和敏感性分析框架。该框架由四个组件组成：数据清洁，代理模型，灵敏度和不确定性分析，以及结果可视化。数据清洁侧重于噪声的拆除，并且可以轻松分析进行必要的变换。基于人工神经网络（ANN）的代理模型是在清洁的船舶运动数据的基础上构建的。对基于ANN代理模型产生的样本或权重进行敏感性和不确定性分析。灵敏度和不确定性分析的结果可以有利于提供数据驱动船舶运动模型的优化。三种独特的敏感性分析（SA）方法（Garson / Morris / Sobol）和基于PDF的和CDF的不确定方法，以两种类型的船舶运动数据集调查，其中没有环境因素。实验结果还证明了所提出的框架可以应用于估计不同数据集中的敏感性和不确定性。

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