| 起落架载荷标定模型的集成学习方法 |
| Ensemble learning method for landing gear load calibration model |
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| 中文摘要: |
| 为解决飞机起落架载荷标定实验使用线性回归建立标定方程结果不理想的问题，考虑到实验中起落架压缩行程和应变片布片位置等因素对标定载荷的非线性影响，运用特征融合、集成学习理论，通过使用AdaBoost和XGBoost非线性回归方法，构建起落架载荷标定模型。首先，通过起落架载荷标定实验获取实验数据，使用主成分分析方法建立输入特征矩阵；其次，构建起落架载荷标定模型，将起落架三向加载载荷分别作为标签向量，训练集和测试集根据随机取样原则划分，使用AdaBoost和XGBoost两种方法训练标定模型；最后，在测试集中对载荷进行拟合预测，并使用均方根误差、平均绝对误差、决定系数、耗时4个评价指标对模型进行评估。实验结果显示，与广泛使用的最小二乘法相比，XGBoost方法建立的标定模型能够更好地拟合加载载荷，在不考虑时效性的场景下XGBoost算法更具优势。研究结果对提高飞机起落架载荷实测准确性以及飞机结构健康监测的进一步研究具有重要价值。 |
| 英文摘要: |
| In order to solve the problem that the calibration equation established by linear regression is not ideal for the aircraft landing gear load calibration experiment, considering the nonlinear influence of factors such as landing gear compression stroke and strain gauge position on the calibration load in the experiment, the landing gear load calibration model is constructed by using AdaBoost and XGBoost nonlinear regression methods using feature fusion and ensemble learning theory. Firstly, experimental data are obtained through landing gear load calibration experiments, and the input feature matrix is established using principal component analysis method. Then, a landing gear load calibration model is constructed, using the three directional loading loads of the landing gear as label vectors. The training and testing sets are divided according to the random sampling principle, and the calibration model is trained using AdaBoost and XGBoost methods. Finally, the load is fitted and predicted in the testing set, and the model is evaluated using four evaluation indicators: root mean square error, average absolute error, determination coefficient and time. The experimental results show that compared with the widely used least squares method, the calibration model established by XGBoost method can better fit the loading load. XGBoost algorithm is more advantageous in scenarios without considering timeliness. The research results have important value for improving the accuracy of aircraft landing gear load measurement and further research on aircraft Structural health monitoring. |
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| 中文关键词: 飞机起落架 载荷标定 非线性回归 集成学习 |
| 英文关键词:landing gear load calibration nonlinear regression ensemble learning |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2023.02.02 |
| 引用本文：倪天琦, 田永卫, 王勇, 齐贺阳, 辛育霞, 刘家旭.起落架载荷标定模型的集成学习方法[J].计测技术,2023,(2):. |
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