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Localization of CO\(_2\) gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling

Han, Xiaojuan, Zhao, Song, Cui, Xiwang, Yan, Yong (2019) Localization of CO\(_2\) gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling. Measurement Science and Technology, 30 (8). Article Number 085007. ISSN 0957-0233. (doi:10.1088/1361-6501/ab1025) (KAR id:76654)

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https://doi.org/10.1088/1361-6501%2Fab1025

Abstract

CO\(_2\) leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO\(_2\) pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO\(_2\) pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO\(_2\) pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects.

Item Type: Article
DOI/Identification number: 10.1088/1361-6501/ab1025
Uncontrolled keywords: Leak detection; Acoustic emission; Carbon capture and storage; Wavelet packet; Radial basis function network
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Yong Yan
Date Deposited: 20 Sep 2019 08:54 UTC
Last Modified: 16 Feb 2021 14:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/76654 (The current URI for this page, for reference purposes)
Yan, Yong: https://orcid.org/0000-0001-7135-5456
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