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Wireless Communications and Mobile Computing
Volume 2018, Article ID 6934825, 8 pages
https://doi.org/10.1155/2018/6934825
Research Article

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

1School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China
2School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
3School of Petrochemical Engineering, Liaoning Shihua University, Fushun 113001, China
4CNPC Northeast Refining & Chemical Engineering Co. Ltd. Shenyang Company, Shenyang 110167, China

Correspondence should be addressed to Zhiyong Hu; moc.361@420gnoyihzuh

Received 5 April 2018; Accepted 27 June 2018; Published 16 August 2018

Academic Editor: Houbing Song

Copyright © 2018 Xianming Lang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression () to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.