Temperature Compensation and Amplitude Prediction in Ultrasonic Measurement Based on BP Neural Network Mode

Wanjia Gao, Fei Li, Ran Yang, Wenyi Liu
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引用次数: 1

Abstract

In the system that uses ultrasonic to measure the liquid level, the sound velocity of the ultrasonic will be changed due to the difference in temperature, which will affect the reliability of the experimental results. This paper explores the relationship between temperature and sound velocity, and acoustic impedance. Based on that, the temperature effects on the ultrasonic signals are explored. And this paper fits the evaluations to the temperature compensation formula with the ordinary least squares. Then this paper builds a temperature-amplitude prediction model based on the BP neural network using the received ultrasonic echo data. The evaluations show that as the temperature increases, the received echo amplitude decreases. The voltage drops by 10 mV for every 5°C increase in temperature. The measurement accuracy is $\pm 1\lt C$. Its linear fitting r2 is 0.99326. The amplitude prediction model built by BP neural network has a prediction accuracy R2 of 0.96887. The error between the predicted value and the ground truth is less than 4.09%. The research can effectively predict the amplitude of data collected at different temperatures. It provides an effective temperature compensation reference in the experiments based on ultrasonic.
基于BP神经网络的超声测量温度补偿与振幅预测
在使用超声波测量液位的系统中,由于温度的差异,超声波的声速会发生变化,从而影响实验结果的可靠性。本文探讨了温度与声速、声阻抗之间的关系。在此基础上,探讨了温度对超声信号的影响。并用普通最小二乘法对温度补偿公式进行了拟合。然后利用接收到的超声回波数据,建立了基于BP神经网络的温度-振幅预测模型。计算结果表明,随着温度的升高,接收到的回波幅度减小。温度每升高5℃,电压下降10mv。测量精度为$\pm 1\lt C$。其线性拟合r2为0.99326。BP神经网络建立的振幅预测模型预测精度R2为0.96887。预测值与地面真实值的误差小于4.09%。该研究可以有效地预测不同温度下采集数据的振幅。为基于超声的温度补偿实验提供了有效的参考依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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