Impact Analysis of Inline Inspection Accuracy on Pipeline Integrity Planning
Bibliographic record
Abstract
Abstract Integrity planning methods and inline inspection (ILI) tool performance have a great impact on a pipeline integrity management program. In pipeline integrity planning, risk and integrity assessments are performed to schedule integrity activities like ILI for the purpose of reducing risks and ensuring reliable and safe operations. In this paper, a method is developed for analyzing the impact of ILI tool accuracy on pipeline integrity planning, which is of great importance but has not been systematically studied before. Crack inspection and threat of fatigue cracking are used as the working case for the analysis, although the approach could potentially be used for any pipeline threat type. The Paris' law degradation model is used for the crack growth and subsequent severity and risk assessment. We investigated the impact of ILI tool accuracy on the cost rate, as well as the associated inspection intervals. The impact on long-term cost rate was also investigated considering new defect generation and continuous growth. Sensitivity analyses were performed. The optimal inspection intervals and the corresponding total cost rates with respect to different ILI tool accuracy and different input parameters were obtained and compared. The proposed method can support integrity management program planning by linking risks with integrity plan costs associated with ILI accuracy and optimal re-assessment intervals. The contributions of this paper mainly include the investigation of the problem of how ILI tool accuracy impacts integrity planning, the development of the method for analyzing pipelines with cracks, and the verification and validation with the examples.
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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".