TY - JOUR
T1 - Intelligent Ultrasonic Flow Measurement Using Linear Array Transducer with Recurrent Neural Networks
AU - Nguyen, Thi Huong Ly
AU - Park, Suhyun
N1 - Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under Grant NRF-2020R1A2C1011889.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - To realize high-quality transit-time ultrasonic flow measurements, accurate and precise estimates of the transit-time difference are essential. In this study, we propose deep learning-based neural network (NN) models to measure the transit-time difference in an ultrasonic flowmeter using a linear array transducer. Three approaches to compute the transit-time difference are presented: the cross-correlation with phase zero-crossing (XCorr), fully connected NN, and recurrent neural network (RNN) with long short-term memory (LSTM). The training data for the NN were generated by simulating target time differences by utilizing the experimental data acquired in the pipe system. To evaluate the performance of the proposed methods, linear regression, the Bland-Altman plot, and the root mean squared error (RMSE) were analyzed using testing data from the experiment. The results of this study show that the RNN-based approach yielded improved performance with an accuracy of up to 94% and a 33.48% reduction in the RMSE, compared to the XCorr method. In addition to the time difference estimation, our proposed RNN-based model can replace the entire flow rate estimation process, including interpolation, velocity correction, and zero-flow calibration. This study demonstrates the feasibility of an intelligent ultrasonic flowmeter employing the RNN-based model with potential in industrial applications.
AB - To realize high-quality transit-time ultrasonic flow measurements, accurate and precise estimates of the transit-time difference are essential. In this study, we propose deep learning-based neural network (NN) models to measure the transit-time difference in an ultrasonic flowmeter using a linear array transducer. Three approaches to compute the transit-time difference are presented: the cross-correlation with phase zero-crossing (XCorr), fully connected NN, and recurrent neural network (RNN) with long short-term memory (LSTM). The training data for the NN were generated by simulating target time differences by utilizing the experimental data acquired in the pipe system. To evaluate the performance of the proposed methods, linear regression, the Bland-Altman plot, and the root mean squared error (RMSE) were analyzed using testing data from the experiment. The results of this study show that the RNN-based approach yielded improved performance with an accuracy of up to 94% and a 33.48% reduction in the RMSE, compared to the XCorr method. In addition to the time difference estimation, our proposed RNN-based model can replace the entire flow rate estimation process, including interpolation, velocity correction, and zero-flow calibration. This study demonstrates the feasibility of an intelligent ultrasonic flowmeter employing the RNN-based model with potential in industrial applications.
KW - Deep learning
KW - recurrent neural network
KW - transit-time estimation
KW - ultrasonic flowmeter
UR - http://www.scopus.com/inward/record.url?scp=85089585115&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3012037
DO - 10.1109/ACCESS.2020.3012037
M3 - Article
AN - SCOPUS:85089585115
SN - 2169-3536
VL - 8
SP - 137564
EP - 137573
JO - IEEE Access
JF - IEEE Access
M1 - 9149600
ER -