ILI Performance- Validating Rupture Pressure Prediction Performance of In-Line Inspection Tools
Bibliographic record
Abstract
Abstract Successful application of in-line inspection (ILI) data for assessing the integrity of pipelines depends on understanding the performance of the specific technology employed. Actual performance of these technologies can vary from that claimed by the inspection tool vendor depending on a number of pipeline design, construction and operational variables. Consideration of ILI performance for magnetic flux leakage based metal loss tools is often limited to accuracy of metal loss depth and positional measurements but depth is only one measurement generally to be considered, the other is a prediction of burst pressure for corroded pipe. Metal loss depth at 80% confidence within +/-10% wall thickness is an often stated performance for ILI technologies. There are no performance claims for accuracy in burst pressure performance because ILI measures defect dimensions that are used to calculate burst but there are many other inputs to calculating burst pressure. However, an understanding of actual in-line inspection tool performance can help pipeline operators gauge the relative level of conservatism associated with decisions to accept or reject metal loss features based on an ILI log prediction. Accurate and reliable correlation of burst pressure predictions from ILI with direct examination predictions depends on matching of appropriate areas of corrosion as well as the accuracy of the inditch methods used. Complex areas of corrosion can be difficult to match with ILI predictions and introduce possible error in validation correlations. This paper examines the practical technical issues involved in making validation comparisons between in-line inspection predictions and in-ditch validation and presents new data analysis tools and techniques, particularly applicable to high resolution laser and ultrasonic direct examination technologies that can be employed to increase accuracy and reliability of burst pressure validation.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".