Using Machine Learning Tools to Enhance Signal Analysis Efficiency in Heat Exchanger Tube Inspection
Bibliographic record
Abstract
The nondestructive testing (NDT) industry is facing a shortage of skilled workers, particularly in heat exchanger tube inspection, where there is a high demand for experts who can analyze collected data. As new technologies emerge and the industry shifts to array probe configurations, task complexity and data volume continue to increase. This highlights the need for tools that simplify data analysis, improve efficiency, and provide reliable results. Although there are a few assisted analysis tools for tube inspection, most of the solutions available are adapted to eddy current testing (ECT) for steam generator tube inspection. Traditional detection methods, relying on phase angle, amplitude thresholds, and probe positioning, become limited under less-than-ideal conditions, such as uneven pulling speed, incomplete scans, or missing encoder data. Additionally, incomplete tube bundle information, like missing landmark tables or re-tube section details, complicates analysis. These challenges are especially common outside the nuclear sector and can affect data reliability. To address these issues, Eddyfi Technologies has developed an AI-powered assisted analysis tool. This tool enhances several stages of the tube inspection process, such as landmark and defects detection and localization, without needing detailed prior knowledge of tube configuration.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".