Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals
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 te...
Main Authors: | Yingchun Xie, Yucheng Xiao, Xuyan Liu, Guijie Liu, Weixiong Jiang, Jin Qin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/18/5040 |
Similar Items
-
On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks
by: Qiu Wang, et al.
Published: (2016-05-01) -
A Broadband Approach to Measuring Acoustic Impedance and Roughness using Spherical Hydrophones
by: Noonchester, Mark
Published: (2010) -
A Non-Contact Fault Diagnosis Method for Rolling Bearings Based on Acoustic Imaging and Convolutional Neural Networks
by: Ran Wang, et al.
Published: (2020-01-01) -
Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology
by: Shengshan Pan, et al.
Published: (2018-10-01) -
Non-Invasive Methods To Detect Underground Leaks
Published: (2019)