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

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Title: Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.
Authors: Xu, Tianshu1,2 (AUTHOR), Zeng, Zhoumo1,2 (AUTHOR), Huang, Xinjing1,2 (AUTHOR) huangxinjing@tju.edu.cn, Li, Jian1,2 (AUTHOR), Feng, Hao1,2 (AUTHOR)
Source: Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B. Sep2021, Vol. 153, p167-177. 11p.
Subjects: Leak detection, Support vector machines, Noise, Signal detection, Detectors
Abstract: 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 %. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract: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 %. [ABSTRACT FROM AUTHOR]
ISSN:09575820
DOI:10.1016/j.psep.2021.07.024