Flow Measurement of Natural Gas in Pipeline Based on 1D-Convolutional Neural Network

DOI

10.2991/ijcis.d.200803.002How to use a DOI?

Keywords

Flow measurement; Ultrasonic signal; Arrival time; Feature recognition; Convolutional neural network

Abstract

Time-difference method is a vitally significant algorithm for measuring natural gas flow with ultrasonic gas flowmeter. The key of this algorithm is to accurately measure the arrival time of ultrasonic signal. However, it is difficult to determine the feature points corresponding to the arrival time stably and accurately. To solve this problem, based on great feature recognition ability of deep learning, one-dimensional-convolutional neural network (1D-CNN) is utilized to determine the arrival time of ultrasonic signal according to the feature of the arrival time. First of all, a dataset, which includes different features such as different arrival time, different signal-to-noises (SNRs), etc., is used as a training set to train the 1D-CNN. Then, based on the size of the training set, an 1D-CNN is designed which includes three convolution and pooling layers and one fully connected layer to determine the arrival time, and the gas flow rate is calculated. To verify this method, an experimental ultrasonic gas flowmeter system is developed. By comparing with the typical method of determining arrival time, most of the deviations distribute close to zero and less than ±5 us using the proposed 1D-CNN, which verifies the effectiveness of the proposed 1D-CNN method.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Tianjiao Zhang
PY  - 2020
DA  - 2020/08/19
TI  - Flow Measurement of Natural Gas in Pipeline Based on 1D-Convolutional Neural Network
JO  - International Journal of Computational Intelligence Systems
SP  - 1198
EP  - 1206
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200803.002
DO  - 10.2991/ijcis.d.200803.002
ID  - Zhang2020
ER  -