Pipeline SCADA Data Recording, Storing, and Filtering for Crack-Growth Analysis
Bibliographic record
Abstract
The Supervisory Control and Data Acquisition (SCADA) data are primarily used for pipeline-pressure monitoring and control. This investigation aims to develop improved methods of recording, storing, and filtering SCADA data for the purpose of predicting crack growth and remaining lifetime of both oil- and gas-pipeline steels experiencing stress corrosion cracking (SCC) and corrosion fatigue using a computational software. To ensure the modeling accuracy, the maximum time intervals for SCADA data collection were investigated, to reduce data storage and calculation time. SCADA data are to be recorded at appropriate sampling intervals to capture all pressure events that could affect crack growth, while the data should be minimized to reduce the time needed for crack-growth calculation without compromising the accuracy of prediction. In this work, it was proposed to record a set of data consisting of one maximum and one minimum point of pressure within a given sampling interval of 1 min (for oil pipelines) and 2 h (for gas pipelines). Screening models to determine and to remove unrealistic SCADA data because of either electronic noise or system errors have also been developed for both oil and gas SCADA data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".