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

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ID: 69768
2018
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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 (DBNICR) 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 DBNICR 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.
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lang2018pipelinewireless Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lang, Xianming;Hu, Zhiyong;Li, Ping;Li, Yan;Cao, Jiangtao;Ren, Hong;
Journal wireless communications and mobile computing
Year 2018
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2018/6934825
Keywords
Technology Mechanical engineering and machinery Science physics telecommunication electronics geophysics. cosmic physics

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