1-20 of 26
Artificial neural networks
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Diagnosis and Recognition of Pipeline Damage Defects Based on Neural Network Algorithm

Min Zhang, Yanbao Guo, Deguo Wang, Qiang Du
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T01A013, September 26–30, 2022
Paper No: IPC2022-87163
DOI: https://doi.org/10.1115/IPC2022-87163
View Paper titled, Diagnosis and Recognition of Pipeline Damage Defects Based on Neural Network Algorithm
Click here to open pdf in another window PDF for Diagnosis and Recognition of Pipeline Damage Defects Based on Neural Network Algorithm
Topics: Algorithms, Artificial neural networks, Damage, Pipelines, Safety
Proceedings Papers

Comparison of Machine Learning Models for Quantitative Risk Modelling of Pipeline Systems

Daryl Bandstra, Juan S. Rojas, Alex Fraser, Mari Shironishi
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A025, September 26–30, 2022
Paper No: IPC2022-87258
DOI: https://doi.org/10.1115/IPC2022-87258
View Paper titled, Comparison of Machine Learning Models for Quantitative Risk Modelling of Pipeline Systems
Click here to open pdf in another window PDF for Comparison of Machine Learning Models for Quantitative Risk Modelling of Pipeline Systems
Topics: Machine learning, Modeling, Pipeline systems, Risk, Artificial neural networks, Failure, Fittings, Industrial research, Pipelines, Pipes
Proceedings Papers

Probabilistic Analysis Applied to the Risk of SCC Failure

Scott Riccardella, Owen Malinowski, Pete Riccardella, Steve Potts, Sean Moran, Kelly Thompson, Ann Reo
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A005, September 26–30, 2022
Paper No: IPC2022-86906
DOI: https://doi.org/10.1115/IPC2022-86906
View Paper titled, Probabilistic Analysis Applied to the Risk of SCC Failure
Click here to open pdf in another window PDF for Probabilistic Analysis Applied to the Risk of SCC Failure
Topics: Artificial neural networks, Failure, Machine learning, Risk, Probability, Algorithms, Coating processes, Coatings, Compressors, Fracture mechanics
Proceedings Papers

CNN-Based Flow Pattern Identification Based On Flow-Induced Vibration Characteristics For Multiphase Flow Pipelines

Haobin Chen, Zhuoran Dang, Ronald Hugo, Simon Park
Proc. ASME. IPC2022, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A003, September 26–30, 2022
Paper No: IPC2022-87001
DOI: https://doi.org/10.1115/IPC2022-87001
View Paper titled, CNN-Based Flow Pattern Identification Based On Flow-Induced Vibration Characteristics For Multiphase Flow Pipelines
Click here to open pdf in another window PDF for CNN-Based Flow Pattern Identification Based On Flow-Induced Vibration Characteristics For Multiphase Flow Pipelines
Topics: Artificial neural networks, Flow (Dynamics), Flow-induced vibrations, Fourier transforms, Multiphase flow, Pipelines
Proceedings Papers

Reliability-Based Assessment Method of Buried Pipeline at Fault Crossings

Qian Zheng, Xiaoben Liu, Hong Zhang, Samer Adeeb
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, V002T07A019, September 28–30, 2020
Paper No: IPC2020-9609
DOI: https://doi.org/10.1115/IPC2020-9609
View Paper titled, Reliability-Based Assessment Method of Buried Pipeline at Fault Crossings
Click here to open pdf in another window PDF for Reliability-Based Assessment Method of Buried Pipeline at Fault Crossings
Topics: Artificial neural networks, Finite element methods, Pipelines, Pipes, Reliability, Simulation, Databases, Displacement, Finite element model, Buckling
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
View Paper titled, Study on Intelligent Controller Design of Flow Metrological Calibration System
Click here to open pdf in another window PDF for Study on Intelligent Controller Design of Flow Metrological Calibration System
Topics: Calibration, Design, Flow (Dynamics), Intelligent control systems, Control equipment, Flowmeters, Artificial neural networks, China, Valves, Natural gas
Proceedings Papers

Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization

Tao Yu, Peng Dong, Yang Yu, Jinzhou Song, Jie Zhang
Proc. ASME. IPC2020, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A012, September 28–30, 2020
Paper No: IPC2020-9634
DOI: https://doi.org/10.1115/IPC2020-9634
View Paper titled, Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization
Click here to open pdf in another window PDF for Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization
Topics: Crude oil, Optimization, Pipelines, Temperature, Safety, Algorithms, Artificial neural networks, Data mining, Errors, Genetic algorithms
Proceedings Papers

Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network

Shulong Zhang, Wenxing Zhou, Shenwei Zhang
Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A028, September 28–30, 2020
Paper No: IPC2020-9631
DOI: https://doi.org/10.1115/IPC2020-9631
View Paper titled, Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network
Click here to open pdf in another window PDF for Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network
Topics: Artificial neural networks, Compression, Corrosion, Finite element analysis, Pipelines, Pressure
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
View Paper titled, A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network
Click here to open pdf in another window PDF for A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network
Topics: Artificial neural networks, Corrosion, Machine learning, Pipelines, Signals
Proceedings Papers

Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks

Joel Smith, Jaehee Chae, Shawn Learn, Ron Hugo, Simon Park
Proc. ASME. IPC2018, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A016, September 24–28, 2018
Paper No: IPC2018-78426
DOI: https://doi.org/10.1115/IPC2018-78426
View Paper titled, Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks
Click here to open pdf in another window PDF for Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks
Topics: Artificial neural networks, Modeling, Pipelines, Rupture, Transients (Dynamics), Leakage, Pipeline systems, Flow (Dynamics), Licensing, Noise (Sound)
Proceedings Papers

Gas Demand Forecasting Based on Artificial Neural Network

Kazuyoshi Miura, Ritsuo Sato
Proc. ASME. IPC1998, Volume 2: Design and Construction; Pipeline Automation and Measurement; Environmental Issues; Rotating Equipment Technology, 887-893, June 7–11, 1998
Paper No: IPC1998-2103
DOI: https://doi.org/10.1115/IPC1998-2103
View Paper titled, Gas Demand Forecasting Based on Artificial Neural Network
Click here to open pdf in another window PDF for Gas Demand Forecasting Based on Artificial Neural Network
Topics: Artificial neural networks, Pipelines, Sales, Simulation, Engines
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
View Paper titled, Pipeline Fault Diagnosis System Enhancement
Click here to open pdf in another window PDF for Pipeline Fault Diagnosis System Enhancement
Topics: Fault diagnosis, Pipelines, Temperature, Waves, Fluids, Artificial intelligence, Artificial neural networks, Density, Flow (Dynamics), Heat
Proceedings Papers

Prediction of Pipe Wrinkling Using Artificial Neural Network

Z. L. Chou, J. J. R. Cheng, Joe Zhou
Proc. ASME. IPC2010, 2010 8th International Pipeline Conference, Volume 4, 49-58, September 27–October 1, 2010
Paper No: IPC2010-31165
DOI: https://doi.org/10.1115/IPC2010-31165
View Paper titled, Prediction of Pipe Wrinkling Using Artificial Neural Network
Click here to open pdf in another window PDF for Prediction of Pipe Wrinkling Using Artificial Neural Network
Topics: Artificial neural networks, Pipes, Pipelines, Failure, Buckling, Arctic region, Inspection, Risk, Damage, Engineers
Proceedings Papers

Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine

K. K. Botros, M. Cheung
Proc. ASME. IPC2010, 2010 8th International Pipeline Conference, Volume 3, 77-87, September 27–October 1, 2010
Paper No: IPC2010-31016
DOI: https://doi.org/10.1115/IPC2010-31016
View Paper titled, Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine
Click here to open pdf in another window PDF for Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine
Topics: Artificial neural networks, Emissions, Gas turbines, Nitrogen oxides, Proton exchange membranes, Uncertainty, Engines, Temperature, Compressors, Cycles
Proceedings Papers

Monitoring of Natural Gas Pipeline Leaks

Dongliang Yu, Likun Wang, Bin Xu, Hongchao Wang, Min Xiong, Dongjie Tan
Proc. ASME. IPC2010, 2010 8th International Pipeline Conference, Volume 3, 457-461, September 27–October 1, 2010
Paper No: IPC2010-31069
DOI: https://doi.org/10.1115/IPC2010-31069
View Paper titled, Monitoring of Natural Gas Pipeline Leaks
Click here to open pdf in another window PDF for Monitoring of Natural Gas Pipeline Leaks
Topics: Leakage, Natural gas distribution, Signals, Waves, Pressure, Acoustics, Pipes, Noise (Sound), Pressure measurement, Wavelets
Proceedings Papers

Prediction of Wax Deposition of Crude Using Statistical and Neural Network Methods

Qiyu Huang, Jun Ma
Proc. ASME. IPC2008, 2008 7th International Pipeline Conference, Volume 4, 341-345, September 29–October 3, 2008
Paper No: IPC2008-64225
DOI: https://doi.org/10.1115/IPC2008-64225
View Paper titled, Prediction of Wax Deposition of Crude Using Statistical and Neural Network Methods
Click here to open pdf in another window PDF for Prediction of Wax Deposition of Crude Using Statistical and Neural Network Methods
Topics: Artificial neural networks, Crude oil
Proceedings Papers

Leak Detection Method Based on Support Vector Machine

XiaoJing Fan, LaiBin Zhang, Wei Liang, ZhaoHui Wang
Proc. ASME. IPC2008, 2008 7th International Pipeline Conference, Volume 1, 517-522, September 29–October 3, 2008
Paper No: IPC2008-64118
DOI: https://doi.org/10.1115/IPC2008-64118
View Paper titled, Leak Detection Method Based on Support Vector Machine
Click here to open pdf in another window PDF for Leak Detection Method Based on Support Vector Machine
Topics: Leakage, Support vector machines, Pipelines, Signals, Algorithms, Artificial neural networks, Risk, Optimization, Pipeline systems, Pressure
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
View Paper titled, Artificial Neural Networks Ensemble Used for Pipeline Leak Detection Systems
Click here to open pdf in another window PDF for Artificial Neural Networks Ensemble Used for Pipeline Leak Detection Systems
Topics: Artificial neural networks, Leakage, Pipelines, Uncertainty, Artificial intelligence, Fuzzy logic
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
View Paper titled, New Approaches to the Automated Analysis of Ultrasonic In-Line Inspection Data
Click here to open pdf in another window PDF for New Approaches to the Automated Analysis of Ultrasonic In-Line Inspection Data
Topics: Inspection, Artificial neural networks, Support vector machines, Algorithms, Artificial intelligence, Metals, Pipelines, Reliability
Proceedings Papers

Building a Leakage Detection System Using Ensembles: A New Way

Carlos H. M. Bomfim, Walmir Matos Caminhas, Benjamim Rodrigues de Menezes, Carlos Alexandre Laurentys de Almeida
Proc. ASME. IPC2004, 2004 International Pipeline Conference, Volumes 1, 2, and 3, 2201-2210, October 4–8, 2004
Paper No: IPC2004-0187
DOI: https://doi.org/10.1115/IPC2004-0187
View Paper titled, Building a Leakage Detection System Using Ensembles: A New Way
Click here to open pdf in another window PDF for Building a Leakage Detection System Using Ensembles: A New Way
Topics: Leakage, Pipelines, Errors, Genetic algorithms, Accidents, Artificial neural networks, Failure, Flow (Dynamics), Flow measurement, Fuzzy logic