| Black swans, red herrings and sacred cows: the zoology of pipeline cracks
|
2020 |
Anderson, Ted |
| Next generation ILI crack inspection service - an operator vendor collaboration for a 26-inch pipeline
|
2020 |
Rodriguez, Rogelio Guajardo |
| Risk-based decision-making supported by machine learning
|
2020 |
Gloven, Michael |
| Calculation of a laser-scan-like 3D defect profile from conventional MFL data
|
2020 |
Palmer, Johannes |
| Replacing hydrotesting of low frequency ERW pipe with an enhanced ILI solution - Eclipse
|
2020 |
Hennig, Thomas |
| Understanding the detection capabilities of an ultrasonic crack ILI robot in a dent
|
2020 |
Rodriguez, Rogelio Guajardo |
| RunComs and randomness
|
2020 |
Anderson, Joel |
| High temperature inline inspection
|
2020 |
Alhamalawi, Nader |
| Pipeline change of service - from dirty to clean in 5 easy steps
|
2020 |
Gourley, Mark |
| ILI run-to-run comparison - bias assessment case study
|
2020 |
Ellinger, Matt |
| Leveraging ILI comparative analysis to accurately determine
|
2020 |
Barkdull, Lisa |
| Improving inline inspection performance with dig feedback
|
2020 |
Belanger, Adrian |
| Influence of line-pipe steel microstructure on NDE yield strength predictive capabilities
|
2020 |
Kornuta, Jeffrey |
| A statistical approach to material verification of expected grade through opportunistic field measurements
|
2020 |
Bellemare, Simon |
| Nondestructive evaluation of strength, toughness and residual stress
|
2020 |
Choi, Seunghun |
| Using deep learning to identify the severity of pipeline dents
|
2020 |
Chakraborty, Ishita |
| Utilization of vendor auditing for continuous improvement of ILI system performance
|
2020 |
Augustus, Karl |
| A review of nondestructive technologies and statics-driven approaches for estimating toughness
|
2020 |
Kiefner, John |
| The current progeny of inline inspection machine learning
|
2020 |
Burden, Dane |
| Closed form probabilistic method for conducting pipeline remaining life assessment from ILI data
|
2020 |
Nelson, Jocelyn |
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