Leak detection and localization techniques in oil and gas pipeline: A bibliometric and systematic review

Engineering Failure Analysis, Journal Year: 2023, Volume and Issue: 146, P. 107060 - 107060

Published: Jan. 14, 2023

Language: Английский

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Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review

Energy Reports, Journal Year: 2023, Volume and Issue: 10, P. 1313 - 1338

Published: Aug. 16, 2023

Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, property damage. Therefore, preserving pipeline integrity is crucial a safe sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that effectively tackle these challenges. This review article mainly focuses on the novelty using deep techniques, specifically artificial neural networks (ANNs), support vector machines (SVMs) hybrid (HML) algorithms, predicting different failures in gas industry. In contrast existing noncomprehensive reviews defects, this explicitly addresses application ML parameters, data reliability purpose. surveys research specific area, offering coherent discussion identifying motivations challenges associated with types defects pipelines. also includes bibliometric analysis literature, highlighting common investigated failures, experimental tests. It in-depth details, summarized tables, failure types, commonly used resources, critical discussions. Based comprehensive aforementioned, it was found approaches, ANNs SVMs, accurately predict compared conventional methods. However, highly recommended combine multiple algorithms enhance accuracy prediction time further. Comparing based field, experimental, simulation various establish reliable cost-effective monitoring systems entire network. systematic expected aid understanding gaps provide options other researchers interested failures.

Language: Английский

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Pipeline leak detection method based on acoustic-pressure information fusion

Measurement, Journal Year: 2023, Volume and Issue: 212, P. 112691 - 112691

Published: March 10, 2023

Language: Английский

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Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction

Applied Energy, Journal Year: 2023, Volume and Issue: 352, P. 121975 - 121975

Published: Sept. 29, 2023

Language: Английский

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A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 24, 2024

Abstract Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, cyber threats. To address these difficulties, proactive maintenance management a new technological strategy are needed to increase safety, reliability, efficiency. A novel neural network model for forecasting the life of dry pipeline system detecting metal loss dimension class that is exposed harsh environment presented in this study handle missing data. The proposed blends strength deep learning techniques with industry-specific expertise. main advantage predict significant predicting classification simultaneously employing Bayesian regularization-based framework when there inputs datasets. intelligent model, trained on four datasets system, health condition high accuracy, even if parameters dataset. using technology generated satisfactory results terms numerical performance, MSE R 2 values closer 0 1, respectively. few cases input data carried out, forecasted each case. Then, developed predicted variables. findings reveal has potential real-world applications oil sector estimating pipelines, parameters. Additionally, multi-model comparative analysis sensitivity incorporated, offering an extensive comprehension prediction abilities beneficial insights into impact variables outputs, thereby improving interpretability reliability our results. could help business plans by lowering chance severe accidents harm better safety reliability.

Language: Английский

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A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Journal of Pipeline Systems Engineering and Practice, Journal Year: 2024, Volume and Issue: 15(2)

Published: Jan. 29, 2024

A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the ages corrodes, it becomes susceptible to deterioration, resulting wastage hazardous damages depending on material transports. To mitigate these risks, implementing a suitable monitoring system essential, enabling early identification of damage minimizing waste potential for incidents. The can be exterior, visual/biological, computational. This paper surveys state-of-the-art approaches also performs experimental analyses with few methods signal/data-driven within computational methods. More precisely, signal processing-based leak localization methods, artificial intelligence-based detection combined are given. implements five 17 implementation helps compare understand significance appropriate noise removal feature extraction. data this analysis collected using acousto-optic sensors from an setup. After implementation, highest observed accuracy 99.14% wavelet packet adaptive independent component analysis-based generalized cross correlation, 98.32% one-dimensional convolutional neural network.

Language: Английский

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Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116857 - 116857

Published: Jan. 1, 2025

Language: Английский

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Pipeline signal feature extraction method based on multi-feature entropy fusion and local linear embedding

Systems Science & Control Engineering, Journal Year: 2022, Volume and Issue: 10(1), P. 407 - 416

Published: April 20, 2022

This paper considers the problem of effective feature extraction acoustic signals from oil and gas pipelines under different working conditions. A pipeline leakage detection method is proposed based on multi-feature entropy fusion local linear embedding (LLE). First, seven kinds commonly used which can reflect characteristics signal better are extracted through experiments, including permutation entropy, envelope approximate fuzzy energy sample dispersion entropy. The seven-dimensional vectors obtained by fusion. Second, LLE algorithm to reduce dimension vector complete secondary extraction. Finally, support machine (SVM) identify conditions pipeline. experimental results show that, compared with other dimensionality reduction methods, single-feature method, types effectively problems false negatives positives in detection.

Language: Английский

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Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System

Mathematics, Journal Year: 2023, Volume and Issue: 11(4), P. 945 - 945

Published: Feb. 13, 2023

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In factories, robotic components are vulnerable to failure due various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection diagnosis (FDD) important keep the operation smooth. Previously, only unloaded-based FDD algorithms were considered for system. environment, robot working under conditions speeds, loads, motions. Hence, reduce domain discrepancy between lab scale real we conducted experimentations conditions. For that purpose, an extensive experimental setup prepared perform series experiments mimicking environmental condition. addition, in previous research work, machine learning (ML) deep (DL) approaches proposed arm component detection. However, issues related DL ML approaches. The models problem-specific, complex computations. model needs huge amount data. composed layers have not been thoroughly explored; result, lacks comprehensive explanation. To overcome these issues, transfer (TL) with diverse scenarios. main contribution increase generalization capabilities PHM context previously available work. VGG16 used because its autonomous feature extractions classification. data collected variety different operating loadings, motion patterns. 1D signal converted 2D (scalogram) TL model. approach shows effective performance has variable

Language: Английский

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Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 237, P. 109369 - 109369

Published: May 4, 2023

Language: Английский

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