| 冉林,熊建军,赵照,左承林,易贤.基于机器学习的电热防除冰表面温度变化趋势预测[J].装备环境工程,2021,18(8):29-35. RAN Lin,XIONG Jian-jun,ZHAO Zhao,ZUO Cheng-lin,YI Xian.Prediction of Surface Temperature Change Trend of Electric Heating Anti-icing and De-icing Based on Machine Learning[J].Equipment Environmental Engineering,2021,18(8):29-35. |
| 基于机器学习的电热防除冰表面温度变化趋势预测 |
| Prediction of Surface Temperature Change Trend of Electric Heating Anti-icing and De-icing Based on Machine Learning |
| 投稿时间：2021-03-25 修订日期：2021-05-08 |
| DOI：10.7643/issn.1672-9242.2021.08.006 |
| 中文关键词: 机器学习 结冰风洞 电热防除冰 KNN近邻回归算法 局部线性加权回归算法中图分类号:V211.73 文献标识码:A 文章编号:1672-9242(2021)08-0029-07 |
| 英文关键词:machine learning icing wind tunnel electric heating anti-icing and de-icing K-nearest neighbor regression algorithm local linear weighted regression algorithm |
| 基金项目:国家自然科学基金(11472296) |
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| Author | Institution |
| RAN Lin | Key Laboratory of Icing and Deicing, China Aerodynamics Research and Development Center, Mianyang 621000, China |
| XIONG Jian-jun | Key Laboratory of Icing and Deicing, China Aerodynamics Research and Development Center, Mianyang 621000, China |
| ZHAO Zhao | Key Laboratory of Icing and Deicing, China Aerodynamics Research and Development Center, Mianyang 621000, China |
| ZUO Cheng-lin | Key Laboratory of Icing and Deicing, China Aerodynamics Research and Development Center, Mianyang 621000, China |
| YI Xian | Key Laboratory of Icing and Deicing, China Aerodynamics Research and Development Center, Mianyang 621000, China |
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| 中文摘要: |
| 针对飞机部件周期控制律电加热防除冰的应用，提出基于机器学习以预测电加热防除冰表面温度的变化趋势。依靠大型结冰风洞及其电加热防除冰控制系统完成防除冰试验，获得有效的试验数据，以通、断电周期为分割单元，将试验数据划分成验证集和训练集。根据电热防除冰过程的换热情况，构建样本的特征参数，利用机器学习的有监督学习方式，选择KNN近邻回归算法和局部线性加权回归算法预测温度变化率，再转换为温度，得到的温度变化与测量数据的线性相关性达到80%以上的高相似度结果，表明使用机器学习可快速预测电热防除冰部件的表面温度变化趋势，且不同的回归算法针对模型不同测温点位置的预测效果存在差异。 |
| 英文摘要: |
| For the application of aircraft component cycle control law electric heating anti-icing, this paper proposes to predict the surface temperature change trend of electric heating anti-icing surface based on machine learning. The large-scale icing wind tunnel and its electric heating anti-icing and de-icing control system is used to complete the test, obtain valid test data, and divide the test data into several samples with the on and off cycles as the division unit. According to the heat exchange of the electric heating anti-icing de-icing process, the characteristic parameters of the sample are constructed. The supervised learning method is used to predict and calculate the temperature change rate and convert it to temperature through the K-nearest neighbor regression algorithm and the local linear weighted regression algorithm. The temperature change obtained is the linear correlation of the measured data reaches a high similarity result of more than 80%, which indicates that the use of machine learning can quickly predict the change trend of the surface temperature of electric heating anti-icing components, and different regression algorithms have different prediction effects for different temperature measurement points of the model. |
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