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Leak Detection and Location Based on ISLMD and CNN in a Pipeline

Authors :
Mengfei Zhou
Zheng Pan
Yunwen Liu
Qiang Zhang
Yijun Cai
Haitian Pan
Source :
IEEE Access, Vol 7, Pp 30457-30464 (2019)
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized cross-correlation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location.

Details

Language :
English
ISSN :
21693536
Volume :
7
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.f2dc2ff9c2b249a49d77a8efdfa38a2b
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2019.2902711