Research on vibration fatigue damage identification of oil and gas pipeline under the condition of measured noise injection

Xie, Yaoguo; Gao, Chengang; Wang, Puzhe; Qu, Xianqiang; Cui, Hongbin

doi:10.1016/j.apor.2023.103512

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…There are various data sources involved in gas pipeline management, including but not limited to pipeline pressure, flow, and temperature data, geographic information system (GIS) data, and realtime monitoring data from sensors [4][5][6]. The fusion of data from multiple sources can provide a more comprehensive view of the system and help assess the operational status and potential risks of pipelines more accurately [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

Cao,

Tan,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Aiming at the current challenges of enormous scale, complex structure, difficult control and frequent accidents of city gas high-pressure pipeline network, there are still three aspects of difficulties in the risk monitoring and control of China’s city gas high-pressure pipeline network, namely, rough data, shallow assessment, and lack of power. This paper proposes an intelligent management system for gas pipelines based on C/S model and J2EE enterprise-level framework, in which the failure warning models of gas leakage, Gaussian plume diffusion, and fire and explosion are established. And the Kalman filter algorithm improved by DS evidence theory is used for intelligent fusion of Multi-source data, analyzing and screening the unified adequate information on data types, extracting state characteristics, classifying warning levels, and developing an integrated and visualized pipeline remote diagnosis and warning platform. In the simulation of the intelligent management system of gas pipeline, when the wind speed is 1.5m/s in winter, the ground surface is a safe area within 12.15m of the gas pipeline. When the maximum wind speed is 10m/s, the upper limit distance of the gas leading to fire and explosion is only 2.43m, and the hazardous range of the gas pipeline jet fire is within 12.69m. Relying on the gas high-pressure pipeline network in L city for practical experiments and applications, it provides technical support and decision-making basis for the construction of intelligent pipeline network, comprehensively improves the risk control capability of city gas high-pressure pipeline network, and has reference significance for the risk control of national city gas high-pressure pipeline network.

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Section: Introductionmentioning

confidence: 99%

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

Cao,

Tan,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…Various scholars in the relevant field have proposed multiple methods for leakage detection in pipeline systems, including visual inspection, negative pressure wave method, eddy current detection, fiber optic grating method, and acoustic methods, among others [4][5][6][7][8][9][10]. Among these, acoustic emission technology, as a novel non-destructive testing technique, has the unparalleled advantages of dynamically, sensitively, and comprehensively recording the irreversible changes in composite materials through elastic waves.…”

Section: Introductionmentioning

confidence: 99%

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

Cui,

Zhang,

et al. 2024

JMSE

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Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

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“…Pipe wall groove structures [1,2] are commonly employed, such as blind holes in the wall of bellows or anechoic structures, or grooves embedded in the pipe to monitor and control parameters such as pressure and flow in the pipe in engineering applications. These structures can cause discontinuities in the pipeline wall, resulting in changes in local flow and leading to vibration, noise, and fatigue of the pipeline [3,4]. It is important to note that these structures should be designed and implemented with consideration for their potential impact on pipeline performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of aerodynamic acoustics in the pipe with variable cross-sections

Zheng,

Miao,

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Research on vibration fatigue damage identification of oil and gas pipeline under the condition of measured noise injection

Cited by 7 publications

References 11 publications

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

Optimization of aerodynamic acoustics in the pipe with variable cross-sections

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