Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector

A spherical detector (SD) is capable of closely approaching a leak point and collecting leak sounds from the inside of a long pipeline, thereby enabling an extremely high leak detection sensitivity. However, acoustic noises arise from collision and friction while the SD is rolling forward, hindering the identification of leak acoustic signals. To address this challenge, this work presents a pipeline leak identification method for an SD based on combining variational mode decomposition (VMD) and a support vector machine (SVM). A leak generation system is set up where the pipe is water-filled, pressurized, and tiltable, and the SD can stand still or roll to collect a sufficient variety of leak sound samples. By decomposing the noisy signals into different modes and selecting the modes with high correlations to reconstruct the signals, the VMD can significantly decrease the collision noise. Additionally, the Mel frequency cepstral coefficients (MFCCs) are extracted and used to constitute a characteristic vector for SVM-based leak recognition. The trained neural network effectively identifies the occurrence of a leak; the recognition accuracy can reach up to 93 %, with a satisfactory specificity of 89.6 %. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

This study proposes a pipeline leak identification method based on VMD and SVM, which effectively reduces the impact of collision noise on leak sound recognition and improves leak detection performance through improved mode decomposition and feature extraction techniques.