Comparison of Multiple Crack Detection In-Line Inspection Data to Assess Crack Growth
Bibliographic record
Abstract
Ultrasonic inline inspection (ILI) tools have been used in the oil and gas pipeline industry for the last 14 years to detect and measure cracks. The detection capabilities of these tools have been verified through many field investigations. ILI ultrasonic crack detection has good correlation with the crack layout on the pipe and estimating the maximum crack depth for the crack or colony. Recent analytical developments have improved the ability to locate individual cracks within a colony and to define the crack depth profile. As with the management of corroding pipelines, the ability to accurately discriminate active from non-active cracks and to determine the rate of crack growth is an essential input into a number of key integrity management decisions. For example, in order to identify the need for and timing of field investigations and/or repairs and to optimize re-inspection intervals crack growth rates are a key input. With increasing numbers of cracks and crack colonies being found in pipelines there is a real need for reliable crack growth information to use in prioritizing remediation activities and planning re-inspection intervals. So as more and more pipelines containing cracks are now being inspected for a second time (or even third time in some cases), the industry is starting to look for quantitative crack growth information from the comparison of repeat ultrasonic crack detection ILI runs. This paper describes the processes used to analyze repeat ultrasonic crack detection ILI data and crack growth information that can be obtained. Discussions on how technical improvements made to crack sizing accuracy and how field verification information can benefit integrity plans are also included.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".