Verification of Internal Corrosion Through ILI and Non-Destructive Testing: Lessons Learned
Bibliographic record
Abstract
Abstract Verification of In line Inspection results is often challenging for pipelines with internal corrosion, especially in areas of extensive corrosion and/or areas with challenging corrosion morphologies such as pinhole corrosion (as defined by the Pipeline Operators Forum [1]). The most reliable method which many operators choose is to rely on cut-outs for the purpose of both mitigation and ILI verification. However, depending on the diameter and the criticality of the pipeline, verification of internal corrosion features from in line inspection may be based exclusively on ultrasonic (UT) non-destructive examination (NDE). These ultrasonic inspection techniques can range from hand-held pencil probes to automated scanning systems. The output from these methods does vary both in quantity and quality which can and does impact the potential tool validation as per API 1163 [2]. This paper outlines the results from these different inspection techniques and then compares it to laser scan and pit gauge measurements once the inner surface of the pipe wall is available. This work was done across multiple pipeline diameters and various internal corrosion mechanisms. It reviews the strengths and weakness of the individual NDE measurements and compares them to the original Magnetic Flux Leakage (MFL) inspections from the perspective of validating the inline inspection. An output from this study is a series of guidelines that should be used when verifying internal corrosion that both operators and tool providers can use to determine tool performance. These guidelines help to verify both the ILI tool performance as well as the relevant non-destructive method.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".