An ILI Based Program That Prevents Reoccurrence of Post ILI Failures Seen in Industry
Bibliographic record
Abstract
The pipeline industry has been using Inline Inspection (ILI) since the 1970s. High resolution tools have been available for inspecting corrosion from about the 1980s and related ILI-based programs have been evolving. In this study incident rate data from the last 30 to 40 years of experience was examined and trended. Corrosion related incident rates have reduced where ILI programs have been implemented. Significant changes in programs have shown related incident reductions or positive trends. Throughout this time there have been a few post-ILI incidents and by taking a closer look at these incidents and learning from the findings the ILI-based assessments and programs were further improved. In this study, all of the post-ILI corrosion related ruptures on the TransCanada system have been closely examined and trended. The effects of program changes and related changes to performance indicators have been examined. Some significant industry failures, where data is publicly available, have also been examined. These failures have been analyzed and trended to understand significant commonalities between these failures. Data was analyzed with the intention of learning from them and applying this learning to avoid similar failures in the future. By understanding the uncertainties, technology limitations, and limits of applicability as well as the types of programs used and where these have not identified probable failures practical solutions were derived. All of the failures have been examined (as allowed by the data available) to find approaches which would have proactively identified these events, so that similar events can be avoided in the future. ILI tools generate a wealth of information and appropriate use of this information has shown to be effective in managing pipelines. However, it is also important to understand the limitations of technologies, learn from the failures, and acknowledge uncertainties so that undesirable events can be avoided.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".