| 基于深度学习的爆炸冲击波信号重构模型 |
| Research on reconstruction model of explosion shock wave signal based on deep learning |
| |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 中文摘要: |
| 针对爆炸冲击波信号重构问题，引入深度卷积神经网络（DCNN）捕捉冲击波信号的局部信息和高阶特征，引入双向长短期记忆网络（Bi-LSTM）捕捉冲击波超压数据时序依赖关系，进而构建了基于深度学习的爆炸冲击波信号重构模型。相关实验研究表明，本文构建的爆炸冲击波信号重构模型，综合考量了信号的时序关系、频谱特征、数据变化规律等特征信息；在基于有限测点数据的冲击波场压力分布重构实验中，模拟和实测超压峰值平均误差分别为3.53%和13.71%，正压作用时间平均误差分别为7.35%和14.26%，比冲量平均误差分别为4.02%和11.92%；在基于残缺数据的冲击波压力曲线重构实验中，模拟和实测信号重构的缺失值与原始值基本吻合，且偏差均在0附近；均满足爆炸冲击波压力重构指标要求。研究结果对爆炸冲击波信号重构有重要指导意义。 |
| 英文摘要: |
| Aiming at the reconstruction of explosion shock wave signal， the deep convolutional neural network （DCNN） was introduced to capture the local information and higher-order features of the shock wave signal， and the bi-directional long-term and short-term memory network （Bi-LSTM） was introduced to capture the time series dependence of shock wave overpressure data， and then the reconstruction model of explosion shock wave signal based on deep learning is constructed. The experimental results show that the reconstruction model of explosion shock wave signal constructed in this paper comprehensively considers the characteristic information of signal such as time sequence relationship， spectral characteristics and data variation law. In the pressure distribution reconstruction experiment of shock wave field based on finite measuring point data， the average errors of simulated and measured overpressure peaks are 3.53% and 13.71%， the average errors of positive pressure time are 7.35% and 14.26%， and the average errors of specific impulse are 4.02% and 11.92%， respectively. In the reconstruction experiment of shock wave pressure curve based on incomplete data， the missing values of simulated and measured signals are basically consistent with the original values， and the deviations are all around zero. All meet the requirements of explosion shock wave pressure reconstruction index. The research results have important guiding significance for signal reconstruction of explosion shock wave. |
| 作者 | 单位 | | 孙传猛1,2, 裴东兴1,2, 陈嘉欣1,2, 许瑞嘉1,2, 崔春生1,2, 高群昌2 | 1.中北大学 省部共建动态测试技术国家重点实验室，山西 太原 030051 2.中北大学 电气与控制工程学院，山西 太原 030051 |
|
| 中文关键词: 动态测试 冲击波超压 信号重构 深度学习 |
| 英文关键词:dynamic test shock wave overpressure signal reconstruction deep learning |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2022.02.07 |
| 引用本文：孙传猛1,2, 裴东兴1,2, 陈嘉欣1,2, 许瑞嘉1,2, 崔春生1,2, 高群昌2.基于深度学习的爆炸冲击波信号重构模型[J].计测技术,2022,(2):. |
| 关闭 |
|
|
|
