Research on an Automated Process to Automatically Correlate ILI Features With NDT Laser Scans
Bibliographic record
Abstract
Abstract New Century Software has completed a research and development project in collaboration with TC Energy to automatically correlate ILI corrosion features with corresponding NDT laser scan data to validate ILI depth predictions with raw laser scan pit measurements and generate unity plots. The research project utilized a process that New Century developed that automatically compares ILI features with NDT 3D laser scan measurements to generate unity plots. This process automatically reads the ILI pipe tally reports and generates GIS graphics representing the pipe surface using actual pipe length and diameter dimensions. The process automatically reads raw laser scan data and overlays this data on the GIS pipe surface. The program automates an optimization algorithm that seeks to align the ILI features with the laser data to account for measurement differences between the ILI tool and the laser scan report. Each ILI feature is aligned and used to calculate the measured maximum depth. The ILI predicted depths and the laser measured depths are represented by a unity plot that illustrates the correlation of ILI-predicted features with 3D laser scan measured features. The goal of this project was to identify the best technical approach to automatically match these data and to provide recommendations to TC Energy through testing the technique on a large amount of data and fine-tuning the configuration settings in the algorithm. Also, quantitative approaches were developed and implemented to evaluate the automatically-generated unity plots. Automating this process will save significant time and cost over manual correlation methods and provide pipeline operators with information about the quality of ILI inspections from different vendors. It could also be used to create a bias factor for ILI Corrosion Growth Rate assessment as well as inform the pipeline risk assessment model.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".