Automating Safety Critical Ultrasonic Data Analysis with a Variational Auto-Encoder
Bibliographic record
Abstract
Non-destructive evaluation based on the analysis of normal beam single probe pulse echo ultrasonic data is often a core component of a fitness for service argument supporting the continued use of manufactured components in safety critical contexts. Typical applications include testing of pipe walls, weldments, and concrete structure material condition. We model the analysis process with probabilistic statements and implement them by leveraging the empirically observed capacity of variational auto-encoders to learn and characterize complex distributions. The variational auto-encoder based characterization of ultrasonic datasets allows a partition that effectively removes outlier bias from consideration when applying deterministic methods for parameter estimation. This affords a bias free characterization of nominal response, the direct identification of deviations from it, and obtains real value estimates for the time of flight of features in the dataset. Our results are explainable, do not depend on out of distribution generalization, do not require data-preprocessing, and do not require massive labelled class balanced datasets. As such our method provides an ideal approach to support the automation of normal beam single probe ultrasonic data analysis in safety critical applications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".