Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network

Galvanised Steel Pipe (GSP) is the most common gas pipeline in populated areas. Existing leak detection research aimed at welded steel pipe is not suitable for GSP system due to their differences in line pressure, connection method, and leak path. This paper presents a gas leak detection method for galvanised steel pipe based on acoustic emission. An experimental setup composed of eight segments is designed to quantitatively simulate gas leak in GSP network considering flow-induced noise. The experiments verify that internal flow noise demonstrates similarity to leak-induced signals and thus interferes with leak detection based on shallow machine learning approaches. Convolutional Neural Network (CNN) is therefore introduced to solve the problem. Different network architectures are investigated and evaluated. Two types of inputs are discussed, namely time-domain signal and time-frequency distribution. Leak detection result show that the proposed method is robust to internal flow noise. When leak rate is greater than 0.03 L/s, the best model achieves overall accuracy more than 93 % in both the test set and the cross-validation set. The model performances indicate that traditional frequency analysis is ineffective to improve the flow-noise robustness of the CNN-based leak detector. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

This paper presents a gas leak detection method for galvanised steel pipe based on acoustic emission, which shows strong robustness to internal flow noise and achieves over 93% overall accuracy when the leak rate is greater than 0.03 L/s in both the test set and cross-validation set.