| 顾宇轩,隋福成,宋恩鹏.神经网络技术在单机应变寿命监控中的应用研究[J].装备环境工程,2018,15(12):74-77. GU Yu-xuan,SUI Fu-cheng,SONG En-peng.Application of Neural Network Techniquein Individual Strain Life Monitoring[J].Equipment Environmental Engineering,2018,15(12):74-77. |
| 神经网络技术在单机应变寿命监控中的应用研究 |
| Application of Neural Network Techniquein Individual Strain Life Monitoring |
| 投稿时间：2018-08-11 修订日期：2018-12-25 |
| DOI：10.7643/ issn.1672-9242.2018.12.014 |
| 中文关键词: 应变监控 神经网络 载荷模型 损伤 |
| 英文关键词:strain monitoring neural networks load model damage |
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| 中文摘要: |
| 目的 解决单机应变监控载荷模型中非线性问题带来的误差。方法 基于人工神经网络技术，结合某型飞机典型盒段试验件有限元模型，建立用于单机应变寿命监控的神经网络载荷模型，利用随机选取的测试数据对模型的精确性进行验证，并与多元线性回归模型进行对比分析。结果 BP神经网络载荷模型的预测值比较贴近实测值，同时要优于多元线性回归模型的预测结果。结论 BP神经网络载荷模型可以用于单机应变监控，而且预测精度更高，可以更加准确地把握平尾的损伤情况。 |
| 英文摘要: |
| Objective To solve the error caused by nonlinear problems in the single strain monitoring load model. Methods Based on the technique of artificial neural network and in combination with the finite element model of one airplane’s typical box-section, a neural network load model used for ISM was established to validate its accuracy with random samples. Results The predictive value of BP neural network was close to the measured values of load model; and its prediction result was better than that of the multiple linear regression model. Conclusion The BP neural network load model is suitable for ISM. In addition, because of its high prediction accuracy, the damage of key parts could be monitored more accurately with the usage of neural network load model. |
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