Optimized Methods of Recording Pipeline Pressure Fluctuations for Pipeline Integrity Analysis
Bibliographic record
Abstract
A crack growth and remaining life predictive software Pipe-OnLine has recently been developed to predict crack growth of pipeline steels in near neutral pH environments. Pressure fluctuations from Supervisory Control and Data Acquisition (SCADA) data are utilized as inputs for crack growth calculations. The accuracy of crack growth predictions largely depends on whether the SCADA data have captured all crack-growth contributing events of pressure fluctuations during pipeline operation. This investigation is aimed at 1) to analyse typical characteristics of pressure fluctuations during oil and gas pipeline operations, 2) to model various pressure data recording scenarios in terms of capturing crack growth contributing pressure fluctuation events, and 3) to provide optimized methods for recording pressure data for the purpose of making crack growth and remaining service life predictions. One of the methods being developed requires to take maximum and minimum pressure points within a given sampling interval). By adopting this method, oil pipeline pressures could be recorded at a max time interval of 1 minute, while gas pipeline pressures could be recorded at a time interval up to 2 hours without reducing the accuracy of prediction. This could substantially reduce the size of data storage and shorten the time of data-analysis for life prediction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".