Pipeline SCADA Data Recording, Storing, and Filtering for Crack-Growth Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it