Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning

Machines, Journal Year: 2024, Volume and Issue: 12(1), P. 42 - 42

Published: Jan. 8, 2024

Pipeline integrity and safety depend on the detection prediction of stress corrosion cracking (SCC) other defects. In oil gas pipeline systems, a variety corrosion-monitoring techniques are used. The observed data exhibit characteristics nonlinearity, multidimensionality, noise. Hence, data-driven modeling have been widely utilized. To accomplish intelligent enhance control, machine learning (ML)-based approaches developed. Some published papers related to SCC discussed ML their applications, but none works has shown real ability detect or predict in energy pipelines, though fewer researchers tested models prove them under controlled environments laboratories, which is completely different from work field. Looking at current research status, authors believe that there need explore best technologies identify clear gaps; critical review is, therefore, required. objective this study assess status learning’s applications detection, gaps, indicate future directions scientific application point view. This will highlight limitations challenges employing for also discuss importance incorporating domain knowledge expert inputs accuracy reliability predictions. Finally, framework proposed demonstrate process condition assessments pipelines.

Language: Английский

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Insights into modern machine learning approaches for bearing fault classification: A systematic literature review

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102700 - 102700

Published: Aug. 10, 2024

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result complete system failure, it accounts for approximately 45 %–50 % failures rotating machinery. Hence, is imperative establish thorough accurate predictive maintenance program that can efficiently foresee prevent mishaps or malfunctions. The literature employed variety techniques approaches, from conventional methods contemporary machine learning (ML) ML-integrated IoT-based solutions, categorise bearing faults. This article provides an overview most recent research models used classification summary highlights various significant challenges current models, issues with function, complexities neural network structure, unrealistic datasets, dynamic working conditions machines, noise dataset, limited data availability, imbalanced datasets. In order tackle problems, researchers have endeavored improve apply different methods, convolutional networks, deep belief LiNet, among others. Researchers primarily developed these approaches using datasets publicly accessible sources. study also identified gaps deficiencies, including imbalance, difficulties integration. nascent technologies field problem diagnosis acknowledged Internet Things-based ML vision-based techniques, which currently their initial phases advancement. Ultimately, puts forth several prospective suggestions recommendations.

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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Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 249, P. 110236 - 110236

Published: May 22, 2024

Language: Английский

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Research on risk assessment of coal and gas outburst during continuous excavation cycle of coal mine with dynamic probabilistic inference

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 190, P. 405 - 419

Published: Aug. 15, 2024

Language: Английский

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Industrial Piping System: Design and Corrosion Protection

Surfaces, Journal Year: 2025, Volume and Issue: 8(1), P. 18 - 18

Published: March 9, 2025

Piping system failures in process industries pose significant financial, environmental, and social risks, with inadequate design corrosion being major contributors. This review synthesizes the academic normative literature on pipeline anticorrosive protection strategies, providing a comprehensive examination of layout determination, material selection, methods for mitigating corrosion. A particular focus is placed organic coating as pivotal strategy reduction, in-depth insights into their selection evaluation criteria. By highlighting best practices advancements this aims to enhance overall integrity safety piping systems. The findings are intended support industry professionals implementing more effective measures prevent improve reliability, while also presenting recent advances current demands.

Language: Английский

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Probabilistic Performance Evaluation of Buried Onshore and Offshore Pipelines Subjected to Earthquakes: Perspectives on Uncertainties

Journal of Pipeline Systems Engineering and Practice, Journal Year: 2025, Volume and Issue: 16(3)

Published: March 25, 2025

Language: Английский

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Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 240, P. 109573 - 109573

Published: Aug. 17, 2023

Language: Английский

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Dynamic risk investigation of urban natural gas pipeline accidents using Stochastic Petri net approach

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 178, P. 933 - 946

Published: Sept. 3, 2023

Language: Английский

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A stochastic model for RUL prediction of subsea pipeline subject to corrosion-fatigue degradation

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 178, P. 739 - 747

Published: Aug. 18, 2023

Language: Английский

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