Underwater pipeline leakage detection via multibeam sonar imagery

2017 ◽  
Vol 141 (5) ◽  
pp. 3917-3917 ◽  
Author(s):  
Wanyuan Zhang ◽  
Tian Zhou ◽  
Dongdong Peng ◽  
Jiajun Shen
Keyword(s):  
Leakage Detection ◽  
Multibeam Sonar ◽  
Underwater Pipeline

scholarly journals Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5040 ◽  
Author(s):  
Yingchun Xie ◽  
Yucheng Xiao ◽  
Xuyan Liu ◽  
Guijie Liu ◽  
Weixiong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production System ◽  
Acoustic Signals ◽  
Frequency Noise ◽  
Spectral Structure ◽  
Leakage Detection ◽  
Time Frequency ◽  
Underwater Pipeline ◽  
Subsea Production System

Detection technology of underwater pipeline leakage plays an important role in the subsea production system. In this paper, a new method based on the acoustic leak signal collected by a hydrophone is proposed to detect pipeline leakage in the subsea production system. Through the pipeline leakage test, it is found that the radiation noise is a continuous spectrum of the medium and high-frequency noise. Both the increase in pipe pressure and the diameter of the leak hole will narrow the spectral structure and shift the spectrum center towards the low frequencies. Under the same condition, the pipe pressure has a greater impact on the noise; every 0.05 MPa increase in the pressure, the radiation sound pressure level increases by 6-7 dB. The time-frequency images were obtained by processing the acoustic signals using the Ensemble Empirical Mode Decomposition (EEMD) and Hilbert–Huang transform (HHT), and fed into a two-layer Convolutional Neural Network (CNN) for leakage detection. The results show that CNN can correctly identify the degree of pipeline leakage. Hence, the proposed method provides a new approach for the detection of pipeline leakage in underwater engineering applications.

scholarly journals High-resolution multibeam sonar for subsea leakage detection

2013 ◽  
Author(s):  
Gorm Wendelboe ◽  
Simon Barchard ◽  
Eric Maillard ◽  
Leif Bjorno
Keyword(s):  
High Resolution ◽  
Leakage Detection ◽  
Multibeam Sonar

Study on Field Demonstration of High Sensitivity SF6 Leakage Detection Method for Gas Insulated Switchgear

2020 ◽  
Vol 140 (5) ◽  
pp. 409-414
Author(s):  
Masaru Tatemi ◽  
Hisao Inami ◽  
Toshiaki Rokunohe ◽  
Makoto Hirose
Keyword(s):  
Detection Method ◽  
High Sensitivity ◽  
Leakage Detection ◽  
Gas Insulated Switchgear

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline

Leakage Detection in Underground Gas Pipeline

2019 ◽  
Vol 7 (6) ◽  
pp. 732-736
Author(s):  
Renuka Kishor Kale ◽  
S. L. Nalbalwar ◽  
S. B. Deosarkar ◽  
Sachin Singh
Keyword(s):  
Gas Pipeline ◽  
Leakage Detection

Development of Mid-Frequency Multibeam Sonar for Fisheries Applications

2006 ◽  
Author(s):  
John K. Horne ◽  
Christopher D. Jones ◽  
Mike A. Wolfson
Keyword(s):  
Multibeam Sonar
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