Detection of Crack-Related Features Within Dented Pipe Using Electromagnetic Acoustic Transduction (EMAT) Technology
Bibliographic record
Abstract
Mechanical damage has been identified as a significant integrity threat within the Oil & Gas pipeline industry. In addition to deformation, associated secondary pipeline damage may also consist of coating removal, metal removal and cold working of the underlying metal that may result in cracking within the dented area. Detection of cracks within dented areas of the pipe using conventional Ultrasonic Technology (UT) and Magnetic Flux Leakage (MFL) In-line Inspection (ILI) technologies has been of limited success due to the variety of possible feature expressions, sensor design and arrangements, and the related complexity within the underlying physics for detection and characterization. Previous studies have shown the feasibility of Electro Magnetic Acoustic Transduction (EMAT) technology for detecting and characterizing crack related indications within dents on liquid pipelines. This study expands upon experimental investigations using pull through ILI tests on manufactured dents where machined linear indications (notches) were introduced into the dents. In this paper, the performance of EMAT technology for detection and characterization of crack related features in liquids pipelines under real operating conditions is presented. EMAT data were combined with high resolution caliper data, ultrasonic crack inspection data and dent strain assessment data, to demonstrate the EMAT capabilities to enhance pipeline integrity management of dents. Results of field non-destructive examinations are compared to EMAT predicted values to assess the performance of this technology. This study presents a supplementary method of detecting and mitigating coincidental crack related features with dents on liquids pipelines, further enhancing the safety and improving the integrity management of pipelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".