Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR
Joint Authors
Li, Ping
Lang, Xianming
Hu, Zhiyong
Li, Yan
Cao, Jiangtao
Ren, Hong
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-16
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Lang, Xianming& Hu, Zhiyong& Li, Ping& Li, Yan& Cao, Jiangtao& Ren, Hong. 2018. Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1216192
Modern Language Association (MLA)
Lang, Xianming…[et al.]. Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1216192
American Medical Association (AMA)
Lang, Xianming& Hu, Zhiyong& Li, Ping& Li, Yan& Cao, Jiangtao& Ren, Hong. Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1216192
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1216192