论文标题
使用遗传编程算法的燃气轮机轴扭矩和CODLAG推进系统的燃油流量的估计
Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG Propulsion System Using Genetic Programming Algorithm
论文作者
论文摘要
在本文中，已利用了基于条件的基于条件的柴油电气和气体（CODLAG）推进系统的公开可用数据集，用于获得符号表达式，这些表达式可以估算使用基因程序（GP）算法估算燃气轮机轴扭矩和燃料流量。整个数据集由11934个样本组成，分为80:20的培训和测试部分。用于训练GP算法的训练数据集以获得燃气涡轮轴扭矩和燃料流量估计的符号表达式，由9548个样品组成。燃气涡轮轴扭矩和燃料流量估计获得的最佳符号表达式是根据在上述符号表达式上应用数据集的测试部分生成的$ r^2 $得分。数据集的测试部分由2386个样本组成。燃气轮机轴扭矩估计获得的三种最佳符号表达式分别产生$ r^2 $分别为0.999201、0.999296和0.999374。对于燃油流量估计获得的三种最佳符号表达式，分别产生了$ r^2 $分别为0.995495、0.996465和0.996487。
In this paper, the publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been utilized to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their $R^2$ score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated $R^2$ scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated $R^2$ scores of 0.995495, 0.996465, and 0.996487, respectively.
