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A Modified Leakage Localization Method Using Multilayer Perceptron Neural Networks in a Pressurized Gas Pipe

Department of Mechanical and Automotive Engineering, University of Ulsan, 93 Daehak-ro, Nam-Gu, Ulsan 44610, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(9), 1954;
Received: 15 March 2019 / Revised: 1 May 2019 / Accepted: 8 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Ultrasonic Guided Waves)
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Leak detection and location in a gas distribution network are significant issues. The acoustic emission (AE) technique can be used to locate a pipeline leak. The time delay between two sensor signals can be determined by the cross-correlation function (CCF), which is a measure of the similarity of two signals as a function of the time delay between them. Due to the energy attenuation, dispersion effect and reverberation of the leakage-induced signals in the pipelines, the CCF location method performs poorly. To improve the leakage location accuracy, this paper proposes a modified leakage location method based on the AE signal, and combines the modified generalized cross-correlation location method and the attenuation-based location method using multilayer perceptron neural networks (MLPNN). In addition, the wave speed was estimated more accurately by the peak frequency of the leakage-induced AE signal in combination with the group speed dispersive curve of the fundamental flexural mode. To verify the reliability of the proposed location method, many tests were performed over a range of leak-sensor distances. The location results show that compared to using the CCF location method, the MLPNN locator reduces the average of the relative location errors by 14%, therefore, this proposed method is better than the CCF method for locating a gas pipe leak. View Full-Text
Keywords: acoustic emission; nondestructive testing; leakage location; fault diagnosis acoustic emission; nondestructive testing; leakage location; fault diagnosis

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Wu, Q.; Lee, C.-M. A Modified Leakage Localization Method Using Multilayer Perceptron Neural Networks in a Pressurized Gas Pipe. Appl. Sci. 2019, 9, 1954.

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