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.
Highlights
The subsea production system is a technology-intensive field of marine engineering
Hilbert–Huang transform (HHT) and Ensemble Empirical Mode Decomposition (EEMD) have not been combined with deep learning to detect underwater pipeline leakage
An overview of the proposed method for underwater pipeline leakage detection is shown in overview of the with proposed for underwater pipeline leakage is shown in and the time-frequency image connects the acoustics with Convolutional Neural Network (CNN)
Summary
The subsea production system is a technology-intensive field of marine engineering. It is one of the leading technologies in modern marine petroleum engineering [1,2]. Kumar et al [23] designed a method of symmetric single-valued neutrosophic cross-entropy (SVNCE) of Mode Decomposition (VMD) to classify the bearing defects in the centrifugal pump They used the analytical wavelet transform (AWT) to obtain grayscale acoustic images, and fed them into an improved CNN to create a fault diagnosis model [24]. HHT and EEMD have not been combined with deep learning to detect underwater pipeline leakage To address this issue, a fault diagnosis method based on the acoustic leak signal is proposed in this paper. Radiated noise signals are demonstrated in the time-frequency domain through the EEMD algorithm, and the pipeline leakage degree is identified by HHT-CNN
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