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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

Study on Intelligent Controller Design of Flow Metrological Calibration System

Xiong Yin, Kai Wen, Yan Wu, Lei Zhou, Jing Gong
Proc. ASME. IPC2020, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A008, September 28–30, 2020
Paper No: IPC2020-9557
DOI: https://doi.org/10.1115/IPC2020-9557
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Topics: Calibration, Design, Flow (Dynamics), Intelligent control systems, Control equipment, Flowmeters, Artificial neural networks, China, Valves, Natural gas
Proceedings Papers

Liquid Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Steady-State and Transient Operations

Christopher MacDonald, Michael Yang, Shawn Learn, Ron Hugo, Simon Park
Proc. ASME. IPC2020, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A002, September 28–30, 2020
Paper No: IPC2020-9237
DOI: https://doi.org/10.1115/IPC2020-9237
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Topics: Artificial intelligence, Pipelines, Rupture, Steady state, Transients (Dynamics)
Proceedings Papers

Use of Advanced Processing Techniques of High Density LiDAR in Place of Survey for Cost and Schedule Reductions on Early Phase Pipeline Projects: Capital Project Results

Joseph T. Hlady, David Parker
Proc. ASME. IPC2016, Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction and Environmental Issues; Strain Based Design; Risk and Reliability; Northern Offshore and Production Pipelines, V002T02A013, September 26–30, 2016
Paper No: IPC2016-64235
DOI: https://doi.org/10.1115/IPC2016-64235
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Topics: Density, Pipelines, Databases, Artificial intelligence, Delays, Resolution (Optics)
Proceedings Papers

Pipeline Fault Diagnosis System Enhancement

Tang Xiujia
Proc. ASME. IPC1998, Volume 2: Design and Construction; Pipeline Automation and Measurement; Environmental Issues; Rotating Equipment Technology, 835-841, June 7–11, 1998
Paper No: IPC1998-2097
DOI: https://doi.org/10.1115/IPC1998-2097
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Topics: Fault diagnosis, Pipelines, Temperature, Waves, Fluids, Artificial intelligence, Artificial neural networks, Density, Flow (Dynamics), Heat
Proceedings Papers

Artificial Neural Networks Ensemble Used for Pipeline Leak Detection Systems

Inhau´ma N. Ferraz, Ana C. B. Garcia, Fla´via C. Bernardini
Proc. ASME. IPC2008, 2008 7th International Pipeline Conference, Volume 1, 739-747, September 29–October 3, 2008
Paper No: IPC2008-64664
DOI: https://doi.org/10.1115/IPC2008-64664
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Topics: Artificial neural networks, Leakage, Pipelines, Uncertainty, Artificial intelligence, Fuzzy logic
Proceedings Papers

Pipesworld: Applying Planning Systems to Pipeline Transportation

Ruy L. Milidiu´, Frederico dos Santos Liporace
Proc. ASME. IPC2004, 2004 International Pipeline Conference, Volumes 1, 2, and 3, 713-719, October 4–8, 2004
Paper No: IPC2004-0090
DOI: https://doi.org/10.1115/IPC2004-0090
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Topics: Pipelines, Transportation systems, Artificial intelligence, Algorithms, Modeling, Petroleum, Storage
Proceedings Papers

New Approaches to the Automated Analysis of Ultrasonic In-Line Inspection Data

H. Willems, K. Reber, M. Zo¨llner, M. Ziegenmeyer
Proc. ASME. IPC2004, 2004 International Pipeline Conference, Volumes 1, 2, and 3, 1041-1046, October 4–8, 2004
Paper No: IPC2004-0553
DOI: https://doi.org/10.1115/IPC2004-0553
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Topics: Inspection, Artificial neural networks, Support vector machines, Algorithms, Artificial intelligence, Metals, Pipelines, Reliability