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

---

Transient Tests for Checking the Trieste Subsea Pipeline: Toward Field Tests

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(3), P. 374 - 374

Published: Feb. 22, 2024

Subsea pipelines are vital arteries transporting oil, gas, and water over long distances play a critical role in the global resource supply chain. However, they most vulnerable to damage from both human-made natural causes characterized by inherent inaccessibility. As result, routine inspection monitoring technologies, reliable at lowest possible cost, needed ensure their longevity. To fill this need, use of transient-test-based techniques is proposed. In first paper set two companion papers, attention focused on selection appropriate maneuver that generates pressure waves then planned steps—i.e., sequence actions—functional execution transient tests best flow conditions for effective fault detection. A brief review available detection technologies with limitations also offered. Finally, performance proposed procedure evaluated mainly terms stability regime prior test.

Language: Английский

---

A cascaded deep learning approach for detecting pipeline defects via pretrained YOLOv5 and ViT models based on MFL data

Mechanical Systems and Signal Processing, Journal Year: 2023, Volume and Issue: 206, P. 110919 - 110919

Published: Nov. 16, 2023

Language: Английский

---

A Comprehensive Review on Groundwater Contamination Due to Sewer Leakage: Sources, Detection Techniques, Health Impacts, Mitigation Methods

Water Air & Soil Pollution, Journal Year: 2024, Volume and Issue: 235(1)

Published: Jan. 1, 2024

Language: Английский

---

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

---

Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Jan. 23, 2024

Pipelines are crucial for transporting energy sources, yet corrosion especially stress cracking (SCC) poses a complex and potentially catastrophic form of material degradation. Traditional techniques like finite element analysis (FEA) have been utilized SCC prediction, but it suffers from high computational cost limited scalability. Deep learning (DL) with integration FEA leverages large-scale data learn nonlinear patterns prediction. Currently, literature on deep learning-enabled pipelines prediction is scarce, offering insights into this emerging approach lack comprehensive review. This paper reviews investigates the current research directions applications DL-enabled methodologies simulation The importance DL, technique type network also outlined in review paper. delves DL algorithms their ability to grab interactions between mechanical stress, properties, environmental factors. Based review, was found that proven be strong tools accuracy lower training cost. being replace time-consuming methods conventional codes. Furthermore, article discusses potential integrated enhancing efficiency leading improved pipeline integrity management practices.

Language: Английский

---

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2699 - 2699

Published: April 24, 2024

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in field of ultrasonic defect detection based bibliometric methods, this study employs visual analysis to identify most influential countries, academic institutions, journals domain. Through cluster analysis, it determines primary trends, research hotspots, future directions critical field. Starting from current industrial in-line inspection (ILI) level, paper provides a flowchart selecting methods table comparison, detailing comparative performance limits different devices. It offers comprehensive perspective latest pipeline technology laboratory experiments practice.

Language: Английский

---

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(3), P. 417 - 417

Published: Feb. 26, 2024

Offshore oil and gas resources play a crucial role in supplementing the energy needs of human society. The crisscrossing subsea pipeline network, which serves as vital infrastructure for storage transportation offshore gas, requires regular inspection maintenance to ensure safe operation prevent ecological pollution. In-line (ILI) techniques have been widely used detection potential hazards within network. This paper offers an overview ILI pipelines, examining their advantages, limitations, applicable scenarios, performance. It aims provide valuable insights selection technologies engineering may be beneficial those involved integrity management planning.

Language: Английский

---

Characterization of Oil and Gas Pipeline Detection Signals with Adjustable Weak Magnetic Excitation

Sensing and Imaging, Journal Year: 2025, Volume and Issue: 26(1)

Published: Feb. 3, 2025

Language: Английский

---

A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 2943 - 2943

Published: March 8, 2025

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation progression. Nonetheless, uncertainties tools processes present ILI influence feature accuracy. This study proposes new extensible model consecutive ILIs clustering. By dynamically segmenting into spatially localized clusters, this framework enables isolated pairs merging defects, as well more precise transformations. Moreover, clustering technique—directional epsilon neighborhood (DENC)—is proposed. DENC utilizes spatial graph structures directional proximity thresholds address variability while effectively identifying outliers. The evaluated six segments with varying complexities, achieving high recall precision 91.5% 98.0%, respectively. In comparison exclusively point models, work demonstrates significant improvements terms accuracy, stability, managing interactions adjacent defects. These advancements establish for automated contribute enhanced integrity management.

Language: Английский

---