Protecting HSE: Monitoring Pipe Bending Stress Using Temperature Compensated Distributed Fiber-Optic Strain Sensor for Enhancing Pipeline Integrity Situational Awareness
Bibliographic record
Abstract
Abstract A distributed fiber-optic strain and temperature sensor (DSTS) with less than 1mW (Laser Class 1M) of pump power has been developed by controlling depletion of pump beam resulting from coherent interaction of probe and depleted pump (CIPDP). DSTS with CIPDP technique ensures flat-noise-floor over entire fiber length, which offers same resolution and accuracy over entire fiber length and makes simultaneous measurement of strain and temperature possible. Bending stress monitoring of an 11km pipeline located in northern Alberta, Canada using temperature compensated distributed fiber-optic strain sensor with CIPDP technique is presented. The results showed that some parts of the pipeline experienced 3,063µε during winter from November 2022 when the pipeline was installed to April 2023 when the soil was still frozen. The pipeline leaks due to the ambient temperature change are presented also. The leak volume of 2.187gal with the injection pressure of 105psi and the temperature difference of 39°F (21.6°C) between the line temperature and the soil temperature has been detected in one minute after a leak occurred through 1/8" orifice. All leaks with different volumes controlled by injection pressure, orifice, line temperature, and duration have been detected successfully.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".