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Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology

1,2, 1,2, 1,2,* and 1,2
1
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2
School of Civil Engineering, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3628; https://doi.org/10.3390/s18113628
Received: 17 August 2018 / Revised: 18 October 2018 / Accepted: 20 October 2018 / Published: 25 October 2018
(This article belongs to the Special Issue Smart Sensors and Smart Structures)
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Abstract

Because of the inconvenience of installing sensors in a buried pipeline, an acoustic emission sensor is initially proposed for collecting and analyzing leakage signals inside the pipeline. Four operating conditions of a fluid-filled pipeline are established and a support vector machine (SVM) method is used to accurately classify the leakage condition of the pipeline. Wavelet decomposition and empirical mode decomposition (EMD) methods are initially used in denoising these signals to address the problem in which original leakage acoustic emission signals contain too much noise. Signals with more information and energy are then reconstructed. The time-delay estimation method is finally used to accurately locate the leakage source in the pipeline. The results show that by using SVM, wavelet decomposition and EMD methods, leakage detection in a liquid-filled pipe with built-in acoustic emission sensors is effective and accurate and provides a reference value for real-time online monitoring of pipeline operational status with broad application prospects. View Full-Text
Keywords: fluid-filled pipe; acoustic emission; support vector machine; leak location fluid-filled pipe; acoustic emission; support vector machine; leak location
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Pan, S.; Xu, Z.; Li, D.; Lu, D. Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology. Sensors 2018, 18, 3628.

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