RISK ANALYSIS OF EX-SPOOL 16″ MOL: AN INSIGHT OF MACHINE LEARNING AND EXPERIMENTAL RESULT

Keyphrases

• API 5L 33%
• Carbon-sulfur 33%
• Chemical Composition 33%
• Corrosion Attack 33%
• Corrosion Degradation 33%
• Corrosion Rate 33%
• Energy Dispersive X-ray Spectroscopy 33%
• Failure Prediction 33%
• Failure Probability 66%
• Hardness Test 33%
• Inspection Machine 33%
• Iron Oxide 66%
• Learning Perspective 33%
• Least Squares Support Vector Regression (LSSVR) 33%
• Linear Regression 33%
• Local Wall Thinning 33%
• Machine Learning 100%
• Manganese 33%
• Material Degradation 66%
• Material Mechanical Properties 33%
• Microstructure-based 33%
• Modeling Results 33%
• Multicollinear 33%
• Optical Emission Spectroscopy 33%
• Pipeline Failure 33%
• Random Forest 33%
• Random Forest Machine Learning 33%
• Remaining Useful Life 33%
• Risk Analysis 100%
• Risk-based Inspection 33%
• Sand Abrasion 33%
• Scanning Electron Microscope 66%
• Siderite 33%
• Spool 100%
• Sulfate-reducing Bacteria 33%
• Supervised Machine Learning 33%
• Tensile Test 33%
• Validation Loss 33%
• X Ray Diffraction 33%

Engineering

• Carbon Manganese 25%
• Corroded Area 25%
• Corrosion Attack 25%
• Corrosion Rate 25%
• Energy dispersive spectrometry 25%
• Experimental Result 100%
• Input Data 25%
• Learning System 100%
• Light Emission 25%
• Mols 100%
• Output Data 25%
• Pearsons Linear Correlation Coefficient 25%
• Random Forest 50%
• Ray Diffraction 25%
• Reducing Bacteria 25%
• Scanning Electron Microscope 50%
• Support Vector Machine 25%
• Tensiles 25%

Material Science

• Carbon Dioxide 33%
• Corrosion 100%
• Emission Spectroscopy 33%
• Energy-Dispersive X-Ray Spectroscopy 66%
• Hematite 66%
• Indentation Hardness Testing 33%
• Manganese 33%
• Scanning Electron Microscopy 66%
• X-Ray Diffraction 33%