In-Line Stress Measurement by the Continuous Barkhausen Method
Bibliographic record
Abstract
This paper describes a novel concept for measuring pipe wall stress. Southwest Research Institute® (SwRI®) pioneered the use of the Barkhausen effect for stress measurement in the 1960s, and the method is still in use today. University researchers in Canada are using the technique for determining stress magnitude and direction by making measurements at quite high resolution. Their technique, which follows from the early SwRI work, requires an alternating magnetic excitation field and an inductive sensor that responds to the Barkhausen magnetic transitions. In contrast, the Continuous Barkhausen concept does not require an alternating excitation field, relying instead on the field transition already present as a magnetic flux leakage (MFL) pig moves through a pipeline. All MFL pigs in use today create Barkhausen noise as they move through the pipeline. The only requirement for using those signals to reveal information about the pipe is to provide suitable sensors and amplifiers to develop a data interpretation procedure. This paper reports on work sponsored by the U.S. Department of Transportation in which SwRI and their commercializing partner, H. ROSEN Engineering, performed laboratory and pull-test experiments to validate the technique using MFL pig hardware in a test line having artificially induced stress anomalies. Details of the technique, laboratory experimental results, and pull-test results are presented, along with recommendations for the application of the method to operating pipelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".