Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

Abstract

Summary Galvanized steel pipes with screw thread connections are widely used in the household part of urban gas distribution system. For such pipes, leakages usually occur at the connections as opposed to the pipe bodies. A leak detection method has been proposed for galvanized steel pipes on the basis of acoustic emission (AE) and neural network. From the viewpoint of engineering application, this work conducts a thorough experimental investigation on the efficiency, accuracy, and applicability conditions of the method. An experimental platform consisting of one trunk pipe and three branch pipes with different diameters is set up to simulate the various leak scenarios. After feature extraction and analysis of the AE signals, a classifier on the basis of back propagation neural network is built for gas leak identification. The applicability of the classifier is investigated quantitatively considering different pipe diameters, number of connections, and leak rate. It validates that the proposed leak detection model can achieve an average accuracy above 95% of the leak with flow above 0.052 L/s on the trunk pipe, on the premise that the signals pass through no more than four screw thread connections. Then, the leak with flow above 0.1 L/s on branch pipes can be detected under the same sensor arrangement.

Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

“…In this methodology, the pressure and flow rate were acquired as original data for ANNs, and the pipe friction factor was used as an input to estimate the leak point. Gong [15] proposed a pipe leak detection method based on acoustic emission (AE) data and neural networks. In this detection method, a leak classifier was built based on a backpropagation neural network after leak feature extraction and analysis from AE signals.…”

Nonintrusive Sensor System Developed Using a Force-Sensing Resistor for Pipeline Integrity Monitoring

“…Conversely, optical fber sensors can detect accurate leakage locations relatively well; nevertheless, a disadvantage is that all optical fbers require replacement when only a few of the fbers attached to the pipes are damaged. Although an approach using the fber Bragg grating temperature-sensing method has been proposed to improve the cost and accuracy of optical fber sensors, it cannot overcome the aforementioned limitations [14]. Recently, approaches using acoustic emissions and neural networks combined with artifcial intelligence theory have been proposed.…”

Condition monitoring and temporal‐spatial assessment of composite pipeline transporting potable water

“…For the intelligent condition assessment of pipeline, several machine learning approaches are applied or have the potential to be applied, such as artificial neural network (ANN), [32][33][34] Bayesian modeling, [35][36][37][38][39] and support vector machine (SVM). 40 Different from those techniques, a type of machine learning methods utilizing ensembles of classifications emerges, including random forest (RF), bagging, and boosting.…”