Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks

• Published in: Journal of vibration and control Vol. 24; no. 18; pp. 4122 - 4129
• Main Authors: Song, YJ, Li, SZ
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-09-2018; SAGE PUBLICATIONS, INC
• Subjects: Acoustic emission; Artificial neural networks; Feature extraction; Frequency domain analysis; Galvanized steel; Galvanized steels; Galvanizing; Leak detection; Loosening; Neural networks; Pipes; Screw threads; Steel; Steel pipes; Transportation; Tubes; pipeline leak detection; screw thread connection; acoustic emission; neural network; Galvanized steel pipe
• ISSN: 1077-5463; 1741-2986
• DOI: 10.1177/1077546317720319

Galvanized steel pipes with screw thread connections are widely used in indoor gas transportation. In contrast with the failure of pipe tubes, leakage in this system is prone to occur in the screw thread connections. Aiming at this specific engineering application, a method based on acoustic emission (AE) and artificial neural networks (ANNs) is proposed to detect small gas leaks. Experiments are conducted on a specifically designed galvanized steel pipe system with the manipulated leak occurring in the screw thread connection to acquire the raw AE data. The features in the time and frequency domains are extracted and selected to establish an ANN model for leak detection. It has been validated that the developed ANN-based leak detector can achieve an identification accuracy of over 98%. It is also verified that the proposed model is effective even when the AE signals due to a small leak pass over two screw thread connections or an elbow connection.