Application of EMD Technology in Leakage Acoustic Characteristic Extraction of Gas‐Liquid, Two‐Phase Flow Pipelines

Abstract

Nowadays, the exploitation and transportation of marine oil and gas are mainly achieved using multiphase flow pipelines. Leakage detection of multiphase flow pipelines has always been the most difficult problem regarding the pipeline safety. Compared to other methods, acoustic detection technology has many advantages and high adaptability. However, multiphase flow pipelines are associated with many noise sources that affect the extraction and recognition of leakage signals. In this study, the mechanism of leakage acoustic source generation in gas-liquid, two-phase pipelines is analyzed. First, an acoustic leakage detection experiment in the multiphase pipelines is conducted. The acoustic signals are divided into two classes in accordance with whether leakage occurs or not. The original signals are processed and analyzed based on empirical mode decomposition (EMD) processing technology. Based on the use of signal processing, this study shows that EMD technology can accurately identify the leakage signal in the gas-liquid, two-phase pipeline. Upon increases in the leakage aperture sizes, the entropy of the EMD information of the acoustic signals gradually increases. Finally, the method of the normalized energies characteristic value of each IMF component is also applied in leakage signal processing. When the liquid flow is maintained constant, the energy values of the IMF components change in a nonlinear manner when the gas flow rate increases. This verifies the feasibility of use of the acoustic wave sensing technology for leak detection in multiphase flow pipelines, which has important theoretical significance for promoting the development of safe and efficient operation in two-phase flow pipelines.

Liquid-filled pipeline leak detection and localization based on multi-scale residual networks

“…Xiao et al [9] and Diao et al [10] performed wavelet decomposition and variational mode decomposition (VMD) decomposition on the signals, respectively, and extracted intrinsic modal functions (IMFs) signal features for pipeline leakage detection. Since machine learning methods need to rely on human experience to pre-extract signal features, signal information can be lost, limiting its application in leak detection [11,12]. Therefore, some scholars have started to study leak detection and localization methods based on deep learning algorithms.…”

Untitled

“…Numerous denoising techniques have been introduced and implemented in the literature as a pre-processing tool outside of the data collecting system (Liu et al, 2018;Khamedi et al, 2019;He et al, 2020;Ji et al, 2018). Fast Fourier Transform (FFT) is a widely used denoising algorithm that is typically included into commercial AE data collecting systems.…”

Denoising Acoustic Emission Signal using Empirical Mode Decomposition Method

“…Numerous denoising techniques have been introduced and implemented in the literature as a pre-processing tool outside of the data collecting system (Liu et al, 2018;Khamedi et al, 2019;He et al, 2020;Ji et al, 2018). Fast Fourier Transform (FFT) is a widely used denoising algorithm that is typically included into commercial AE data collecting systems.…”