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Sensors
Volume 20
Issue 18
10.3390/s20185040
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Article

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

by
Yingchun Xie
*,
Yucheng Xiao
,
Xuyan Liu
,
Guijie Liu
,
Weixiong Jiang
and
Jin Qin
College of Engineering, Ocean University of China, Qingdao 266000, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5040; https://doi.org/10.3390/s20185040
Received: 23 July 2020 / Revised: 31 August 2020 / Accepted: 1 September 2020 / Published: 4 September 2020
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
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Abstract

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. View Full-Text
Keywords: acoustic leak signal; hydrophone; fault diagnosis; time-frequency image; EEMD; CNN acoustic leak signal; hydrophone; fault diagnosis; time-frequency image; EEMD; CNN
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MDPI and ACS Style

Xie, Y.; Xiao, Y.; Liu, X.; Liu, G.; Jiang, W.; Qin, J. Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals. Sensors 2020, 20, 5040. https://doi.org/10.3390/s20185040

AMA Style

Xie Y, Xiao Y, Liu X, Liu G, Jiang W, Qin J. Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals. Sensors. 2020; 20(18):5040. https://doi.org/10.3390/s20185040

Chicago/Turabian Style

Xie, Yingchun, Yucheng Xiao, Xuyan Liu, Guijie Liu, Weixiong Jiang, and Jin Qin. 2020. "Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals" Sensors 20, no. 18: 5040. https://doi.org/10.3390/s20185040

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