Development of an ILI Service for Heavy Wall Pipelines Based on EMAT Technology
Bibliographic record
Abstract
Abstract Electro Magnetic Acoustic Transducer (EMAT) technology based ILI inspections have become vital for maintaining the safety of gas pipelines in order to manage crack threats. This technology has evolved over the past decade and has become widely used. As with any technology, existing EMAT services have limitations based on the physics of EMAT and the approach to the application of the technology in ILI services. This paper will describe how TC Energy and ROSEN collaborated to develop reliable EMAT inspection services for heavy wall pipeline applications in addition to the capability of currently available technology. This technology development process involved advancements in sensor design and fabrication, incorporation of latest electronic developments to enhance magnetization as well as leveraging machine learning techniques to enable EMAT inspection of pipelines up to 16.4 mm wall thickness. The paper will also describe the technology validation process including proof of concept, lab testing, innovative testing process on sample pipe while applying artificial defects that closely mimic natural ones, performance qualification process in line with applicable codes, pilot testing, and performance validation. The full process was executed to conform to typical industry validation requirements and to qualify under operator’s extensive technology validation program. The paper will also review preliminary results of the first pilot application and approval for broader application in pipelines. The paper describes the collaborative efforts of the research and development teams, business line management and key account management at ROSEN as well as the technology validation, threat management, and project management teams at TC Energy. The work has culminated in five inspections completed (48″ and 36″) on the operator’s system in Canada and the USA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".