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Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning

Article ID Journal Published Year Pages File Type
4963463 Applied Soft Computing 2017 15 Pages PDF
Keywords
Safety assessmentdefect sizingPattern recognitionOil and gas pipelinesNeural networksDefect locationMagnetic flux leakageMachine learning
Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
Preview
Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning
Authors
Mohamed Layouni, Mohamed Salah Hamdi, Sofiène Tahar,
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Journal
Applied Soft Computing
Journal: Applied Soft Computing
Related Categories
Safety assessment
defect sizing
Pattern recognition
Oil and gas pipelines
Neural networks
Defect location
Magnetic flux leakage
Machine learning
Artificial Intelligence
Computational Theory and Mathematics
Computer Graphics and Computer-Aided Design
Computer Networks and Communications
Computer Science (General)
Computer Science Applications
Computer Vision and Pattern Recognition
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Human-Computer Interaction
Information Systems
Signal Processing
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