An ILI Based Program That Prevents Reoccurrence of Post ILI Failures Seen in Industry
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it