一种冲击波压力传感器的准静态校准神经网络模型

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (13) : 92-95.

论文

赵传荣，孔德仁1，王胜强2，商飞1

作者信息 +

A kind of neural network model of quasi-static calibration of shock wave pressure sensor

ZHAO Chuan-Rong1; KONG De-Ren1; WANG Shen-Qiang ;SHANG Fei1

Author information +

文章历史 +

摘要

由冲击波压力传感器准静态校准原理，间接比对式校准的精度取决于重锤的落高与压力拟合模型的精度，本文采用RBF神经网络建立了以落高为输入量、冲击波压力峰值为输出量的神经网络模型。选用典型标准压力传感器，在7MPa~30MPa量程范围开展校准实验；通过对测试样本进行分析，结果表明：该神经网络模型预测的最大相对误差不超过0.04%，比多项式拟合模型和指数拟合模型高一个数量级。落高与压力拟合模型引入的不确定度是构成冲击波压力传感器动态测量不确定度的一个重要分量，通过建立高精度的重锤落高与冲击波压力峰值神经网络拟合模型，为进一步提高冲击波压力传感器的测量精度奠定了基础。

Abstract

By the principle of quasi-static calibration of shock wave pressure sensor, the comparison calibration accuracy depended on the accuracy of fitting model between the height of drop-hammer and pressure. The paper applied the RBF neural network to set up the neural network model whose input was the height of drop-hammer and output was the shock wave pressure peak. Choosing a certain type of standard pressure sensor, the calibration experiment was carried out in the range of 7MPa-30MPa. By analyzing the test samples, the result indicated that the maximum relative error of the neural network model is not more than 0.04% and a higher magnitude than the polynomial fitting model and exponential fitting model. The uncertainty introduced by the fitting model between the height of drop-hammer and pressure is an important component of dynamic measurement uncertainty of shock wave pressure sensor. Building the neural network fitting model between the height of drop-hammer and shock wave pressure peak will lay the foundation of improving the measurement accuracy of shock wave pressure sensor.

导出引用

赵传荣,孔德仁1，王胜强2，商飞1. 一种冲击波压力传感器的准静态校准神经网络模型[J]. 振动与冲击, 2017, 36(13): 92-95

ZHAO Chuan-Rong1; KONG De-Ren1; WANG Shen-Qiang ;SHANG Fei1. A kind of neural network model of quasi-static calibration of shock wave pressure sensor[J]. Journal of Vibration and Shock, 2017, 36(13): 92-95

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