scholarly journals Analysis of the acoustic signals backscattered by a tube using the time-frequency representations

2008 ◽  
Author(s):  
Mostafa Laaboubi ◽  
Elhoucein Aassif ◽  
Rachid Latif ◽  
Gerard Maze ◽  
Dominque Decultot ◽  
...  
Keyword(s):  
Acoustic Signals ◽  
Time Frequency

Analysis of the acoustic signals backscattered by a tube using the time‐frequency representations

2008 ◽  
Vol 123 (5) ◽  
pp. 3172-3172
Author(s):  
Mustapha Laaboubi ◽  
Elhoucien Aassif ◽  
Rachid Latif ◽  
Gerard Maze ◽  
Dominique Decultot ◽  
...  
Keyword(s):  
Acoustic Signals ◽  
Time Frequency

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 Underwater object tracking using time frequency signatures of acoustic signals

2014 ◽  
Author(s):  
Digulescu Angela ◽  
Candel Ion ◽  
Ioana Cornel ◽  
Bucur Diana ◽  
Petrut Teodor
Keyword(s):  
Object Tracking ◽  
Acoustic Signals ◽  
Time Frequency ◽  
Underwater Object
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