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Proceedings Papers

A Transparent ASME B31.8-Based Strain Assessment Method Using 3D Measurement of Dent Morphology

Shenwei Zhang, Billy Zhang, Rick Wang

Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A057, September 26–30, 2022

Paper No: IPC2022-87168

DOI: https://doi.org/10.1115/IPC2022-87168

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Topics: Transparency, Pipelines, Arches, Damage, Filters, Filtration, Fourier transforms, Inspection, Membranes, Noise (Sound)

Proceedings Papers

Estimating Measurement Performance With Truncated Data Sets

Jason Skow, Joseph W. Krynicki, Alex Fraser, Gustavo Gonzalez

Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A026, September 26–30, 2022

Paper No: IPC2022-87060

DOI: https://doi.org/10.1115/IPC2022-87060

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Topics: Algorithms, American Petroleum Institute, Errors, Event history analysis, Fracture (Materials), Inspection, Nondestructive evaluation, Pipeline integrity, Pipelines, Risk

Proceedings Papers

Pipeline Defect Detection and Fine-Scale Reconstruction From 3-D MFL Signal Analysis Using Object Detection and Physics-Constrained Machine Learning

W. Steven Rosenthal, Stephen Westwood, Kayte Denslow

Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A060, September 26–30, 2022

Paper No: IPC2022-87313

DOI: https://doi.org/10.1115/IPC2022-87313

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Topics: Flaw detection, Machine learning, Physics, Pipelines, Signals

Proceedings Papers

Applying Advanced Ultrasonic In-Line Inspection Technologies to Effectively Manage Hook Cracks

Cory Wargacki, Wade Forshner, Rogelio Guajardo, Thomas Hennig

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A037, September 28–30, 2020

Paper No: IPC2020-9251

DOI: https://doi.org/10.1115/IPC2020-9251

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Topics: Fracture (Materials), Inspection, Pipelines, Algorithms, Echoes, Computer software, Crack detection, Cracking (Materials), Fracture (Process), Manufacturing

Proceedings Papers

A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network

Jenny Jing Chen, Stephen Westwood, David Heaney

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A068, September 28–30, 2020

Paper No: IPC2020-9331

DOI: https://doi.org/10.1115/IPC2020-9331

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Topics: Artificial neural networks, Corrosion, Machine learning, Pipelines, Signals

Proceedings Papers

Concerted, Computing-Intense Novel MFL Approach Ensuring Reliability and Reducing the Need for Dig Verification

Johannes Palmer, Aaron Schartner, Andrey Danilov, Vincent Tse

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A069, September 28–30, 2020

Paper No: IPC2020-9361

DOI: https://doi.org/10.1115/IPC2020-9361

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Topics: Reliability, Metals, Signals, Optimization, Ambiguity, Artificial intelligence, Geometry, Leakage, Machine learning, Magnetic flux

Proceedings Papers

Improved Inspection Through Full Matrix Capture Technology

Domenico Bellistri, Jeff Vinyard, Cameron Seward

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A047, September 28–30, 2020

Paper No: IPC2020-9595

DOI: https://doi.org/10.1115/IPC2020-9595

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Topics: Human factors, Inspection, Nondestructive evaluation, Shear waves, Probes, Signals, Artificial intelligence, Geometry, Safety, Ultrasonic imaging

Proceedings Papers

Power Spectral Density Analysis of Pipeline Pressures for Probabilistic Assessment

M. J. Rosenfeld, Benjamin Zand, Adam Steiner

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A076, September 28–30, 2020

Paper No: IPC2020-9587

DOI: https://doi.org/10.1115/IPC2020-9587

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Topics: Pipelines, Spectral energy distribution, Pressure, Damage, Signals, Cycles, Errors, Computation, Fatigue analysis, Fatigue cracks

Proceedings Papers

Recommended Procedures for Evaluation and Synthesis of Pipelines Subject to Multiple IMU Tool Surveys

James D. Hart, Nasir Zulfiqar, Ed McClarty

Proc. ASME. IPC2020, Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain Based Design; Risk and Reliability; Northern, Offshore, and Production Pipelines, V002T06A009, September 28–30, 2020

Paper No: IPC2020-9235

DOI: https://doi.org/10.1115/IPC2020-9235

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Topics: Pipelines, Construction, Geometry, Deformation, Inspection, Overlays (Materials engineering), Accelerometers, Certainty, Instrumentation, Landslides

Proceedings Papers

Large Standoff Magnetometry As a Practical Screening and Monitoring Tool for Pipelines Subjected to Geohazards

Tianzong (David) Xu

Proc. ASME. IPC2020, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A020, September 28–30, 2020

Paper No: IPC2020-9584

DOI: https://doi.org/10.1115/IPC2020-9584

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Topics: Pipelines, Stress, Magnetization, Signals, Corrosion, Fracture (Materials), Inspection, Leakage, Nondestructive evaluation, Pacific Ocean

Proceedings Papers

Time Dependence of Hydrogen Induced Cracking of X70 Pipeline Steel Under Severe and Mild Sour Service Conditions Using Ultrasonic Analysis

S. Gawor, J. B. Wiskel, D. G. Ivey, J. Liu, H. Henein

Proc. ASME. IPC2020, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T05A024, September 28–30, 2020

Paper No: IPC2020-9787

DOI: https://doi.org/10.1115/IPC2020-9787

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Topics: Cracking (Materials), Fracture (Process), Hydrogen, Pipelines, Steel, Fracture (Materials), Hydrogen embrittlement, Testing, Corrosion, Iron and steel making

Proceedings Papers

Pipeline Data Analytics: Enhanced Corrosion Growth Assessment Through Machine Learning

Michael Smith, Stefan Cronjaeger, Navid Ershad, Randy Nickle, Matthias Peussner

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A048, September 24–28, 2018

Paper No: IPC2018-78364

DOI: https://doi.org/10.1115/IPC2018-78364

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Topics: Corrosion, Machine learning, Pipelines, Signals, Maintenance, Uncertainty, Engineers, Inspection, Lasers, Leakage

Proceedings Papers

Modeling Pipeline Metal Loss Defects at Tool Speed

Matthew Romney, Adrian Belanger

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A001, September 24–28, 2018

Paper No: IPC2018-78014

DOI: https://doi.org/10.1115/IPC2018-78014

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Topics: Metals, Modeling, Pipelines, Computer simulation, Design, Finite element analysis, Leakage, Algorithms, Corrosion, Flow (Dynamics)

Proceedings Papers

Algorithms for the Strain Based Analysis of Dented Pipelines

Chike Okoloekwe, Muntaseer Kainat, Doug Langer, Sherif Hassanien, Samer Adeeb

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A067, September 24–28, 2018

Paper No: IPC2018-78433

DOI: https://doi.org/10.1115/IPC2018-78433

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Topics: Algorithms, Pipelines, Noise (Sound), Signals, Low-pass filters, Analytical methods, B-splines, Corrosion, Deformation, Fracture (Materials)

Proceedings Papers

Adaptive Ultrasonic Imaging of Electric Resistance Welded Pipeline Seams

Harvey Haines, Lars Hörchens, Jasper Schouten

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A027, September 24–28, 2018

Paper No: IPC2018-78189

DOI: https://doi.org/10.1115/IPC2018-78189

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Topics: Electrical resistance, Pipelines, Ultrasonic imaging, Pipes, Shapes, Reflection, Calibration, Corners (Structural elements), Diffraction, Errors

Proceedings Papers

Non-Contact Geomagnetic Localization of Pipeline Defects Using Empirical Mode Decomposition and Magnetic Gradient Tensor

Tao Zhang, Xinhua Wang, Yingchun Chen, Zia Ullah, Yizhen Zhao

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A006, September 24–28, 2018

Paper No: IPC2018-78258

DOI: https://doi.org/10.1115/IPC2018-78258

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Topics: Pipelines, Tensors, Magnetic anomalies, Random noise, Signals, Computational methods, Dipoles (Electromagnetism), Filters, Magnetic fields, Nondestructive evaluation

Proceedings Papers

Evaluation of Corrosion Growth Prediction Methodologies Using Burst Pressure Comparisons From Repeated In-Line Inspections

Chance Wright, Thomas Dessein, Yanping Li, Suzanne Ward

Proc. ASME. IPC2018, Volume 1: Pipeline and Facilities Integrity, V001T03A007, September 24–28, 2018

Paper No: IPC2018-78294

DOI: https://doi.org/10.1115/IPC2018-78294

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Topics: Corrosion, Inspection, Pressure, Pipelines, Safety, Pipe joints, Signals, Statistical distributions

Proceedings Papers

Acoustic Characteristics of Liquid Hydrocarbon Releases From Buried Pipelines: An Experimental Evaluation

Fernando Castellanos, Mirko van der Baan, Mathew Bussiere, Mark Stephens

Proc. ASME. IPC2018, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A009, September 24–28, 2018

Paper No: IPC2018-78244

DOI: https://doi.org/10.1115/IPC2018-78244

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Topics: Acoustics, Pipelines, Leakage, Soil, Signals, Pipes, Anisotropy, Computation, Fluids, Orifices

Proceedings Papers

Real-Time Continuous Pipeline Integrity Monitoring Utilising Telecommunications Optical Fibre Cables

Peter J. Hayward, Tom A. Fryer

Proc. ASME. IPC2018, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A018, September 24–28, 2018

Paper No: IPC2018-78499

DOI: https://doi.org/10.1115/IPC2018-78499

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Topics: Cables, Optical fiber, Pipeline integrity, Telecommunications, Pipelines, Leakage, Data acquisition systems, Acoustics, Backscattering, Fibers

Proceedings Papers

Application of Machine Learning to Distributed Temperature Sensing (DTS) Systems

Maria S. Araujo, Heath A. Spidle, Shane P. Siebenaler, Samantha G. Blaisdell, David W. Vickers

Proc. ASME. IPC2018, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A024, September 24–28, 2018

Paper No: IPC2018-78640

DOI: https://doi.org/10.1115/IPC2018-78640

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Topics: Machine learning, Temperature, Leakage, Pipelines, Noise (Sound), Signals, Cables, Fibers, Pipes, Temperature measurement

NARROW

A Transparent ASME B31.8-Based Strain Assessment Method Using 3D Measurement of Dent Morphology

Estimating Measurement Performance With Truncated Data Sets

Pipeline Defect Detection and Fine-Scale Reconstruction From 3-D MFL Signal Analysis Using Object Detection and Physics-Constrained Machine Learning

Applying Advanced Ultrasonic In-Line Inspection Technologies to Effectively Manage Hook Cracks

A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network

Concerted, Computing-Intense Novel MFL Approach Ensuring Reliability and Reducing the Need for Dig Verification

Improved Inspection Through Full Matrix Capture Technology

Power Spectral Density Analysis of Pipeline Pressures for Probabilistic Assessment

Recommended Procedures for Evaluation and Synthesis of Pipelines Subject to Multiple IMU Tool Surveys

Large Standoff Magnetometry As a Practical Screening and Monitoring Tool for Pipelines Subjected to Geohazards

Time Dependence of Hydrogen Induced Cracking of X70 Pipeline Steel Under Severe and Mild Sour Service Conditions Using Ultrasonic Analysis

Pipeline Data Analytics: Enhanced Corrosion Growth Assessment Through Machine Learning

Modeling Pipeline Metal Loss Defects at Tool Speed

Algorithms for the Strain Based Analysis of Dented Pipelines

Adaptive Ultrasonic Imaging of Electric Resistance Welded Pipeline Seams

Non-Contact Geomagnetic Localization of Pipeline Defects Using Empirical Mode Decomposition and Magnetic Gradient Tensor

Evaluation of Corrosion Growth Prediction Methodologies Using Burst Pressure Comparisons From Repeated In-Line Inspections

Acoustic Characteristics of Liquid Hydrocarbon Releases From Buried Pipelines: An Experimental Evaluation

Real-Time Continuous Pipeline Integrity Monitoring Utilising Telecommunications Optical Fibre Cables

Application of Machine Learning to Distributed Temperature Sensing (DTS) Systems

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