Abstract
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.
Highlights
The ultrasonic flowmeter has been employed for liquid flow rate measurements in industrial and medical fields, such as in pump stations to ensure the energy efficiency of building management systems and in wrist sensors for blood flow monitoring, respectively, owing to its high sensitivity to flow changes [1]–[5]
XCORR-BASED FLOW RATE ESTIMATION Fig. 6(a) presents the estimated flow rates obtained via the XCorr method for the transit-time estimation with the reference flow rates
The current study demonstrates the feasibility of intelligent transit-time ultrasonic flow measurement using an recurrent neural network (RNN)-based approach
Summary
The ultrasonic flowmeter has been employed for liquid flow rate measurements in industrial and medical fields, such as in pump stations to ensure the energy efficiency of building management systems and in wrist sensors for blood flow monitoring, respectively, owing to its high sensitivity to flow changes [1]–[5]. The transit-time method has widespread applicability for the non-invasive measurement of the flow of particle-free fluids through pipes [6]–[8]. The transit-time ultrasonic flowmeter obtains the fluid flow rate from the transit-time difference of ultrasonic signals between the downstream and upstream of the flow. Ultrasonic signals originate from reflections arising due to the impedance mismatch between the pipe walls and the fluid. Time ultrasonic flowmeter, such as a misalignment of ultrasonic sensors, irregular pipe surface conditions, and errors in the transit-time difference estimation. The accuracy of the ultrasound flowmeter can be improved by reducing errors in the transit-time estimation
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