| 刘佳玮,于明鑫,祝连庆,夏嘉斌,闫 光,梁生珺.基于深度学习的机翼蒙皮载荷计算方法[J].电子测量与仪器学报,2022,36(4):1-8 |
| 基于深度学习的机翼蒙皮载荷计算方法 |
| Calculation method of wing skin load based on deep learning |
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| DOI： |
| 中文关键词: 机翼蒙皮 载荷计算 深度学习 应变 载荷标定方程 |
| 英文关键词:wing skin load calculation deep learning strain load calibration equation |
| 基金项目:国家重点研发计划(啁啾光纤光栅）、高等学校学科创新引智计划项目资助 |
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| Author | Institution |
| Liu Jiawei | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University |
| Yu Mingxin | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University,2. Beijing Key Laboratory of Optoelectronic Measurement Technology, Beijing Information Science & Technology University |
| Zhu Lianqing | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University,2. Beijing Key Laboratory of Optoelectronic Measurement Technology, Beijing Information Science & Technology University,3. Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University |
| Xia Jiabin | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University |
| Yan Guang | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University,3. Beijing Laboratory of
Optical Fiber Sensing and System, Beijing Information Science & Technology University |
| Liang Shengjun | 1. Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University |
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| 摘要点击次数: 1068 |
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| 中文摘要: |
| 针对传统的载荷标定方程计算机翼蒙皮载荷精度低的问题,提出了一种基于深度学习的机翼蒙皮载荷计算新方法。 考
虑真实机翼蒙皮受力复杂,首先建立了机翼蒙皮试验件模型,使用 Ansys 仿真软件对试验件进行有限元分析,获得应变与载荷
仿真数据,并对仿真数据进行数据清洗与预处理;其次,构建深度神经网络模型,将应变与载荷作为神经网络模型的输入与输出
值,采用 Adam 算法优化提出的载荷计算模型;最后,在测试集上对载荷值进行预测,使用平均相对误差与绝对值差作为评价指
标。 实验结果显示,基于深度学习的载荷计算方法在小载荷数据上平均绝对误差为 0. 081 N,在正常载荷数据上的平均相对误
差为 0. 063 8%;与传统载荷计算方法比较,本文提出的新方法计算的载荷精度明显优于传统方法。 |
| 英文摘要: |
| Aiming at the problem of low accuracy of the traditional load calibration equation for calculating wing skin load, a novel
method of wing skin load calculation based on deep learning was proposed. Considering that the force of the real wing skin was
complicated, this paper established a simplified wing structure model. Firstly, the finite element analysis on the wing was carried out by
using Ansys software to obtain the strain and force simulation data, then the simulation data was cleaned and preprocessed. Secondly, a
deep neural network model was constructed, its input and output were the strain and load values, respectively. The Adam optimization
algorithm was used to optimize the model for load calculation. Finally, the test set was used to predict the load value, and the average
relative error and absolute error were used as evaluation metrics. Experimental results show that the calculation method based on deep
learning obtains the average absolute error of 0. 081 N for small load data and average relative error of 0. 063 8% for normal load data,
respectively. The load accuracy of new method is obviously better than that of the traditional method comparing with traditional load
calibration method. |
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