A wavelet-based denoising method for pipeline dent assessments
Bibliographic record
Abstract
Strain-based assessments of dents in buried steel oil and gas pipelines are commonly carried out in practice. Dent signals obtained from the caliper inspection tool contain noises that can have a large impact on the accuracy of the estimated strain. This paper proposes a novel wavelet-based denoising method for dent signals based on the overcomplete expansion with the corresponding dictionary constructed using the stationary and hyperbolic wavelet transforms. The proposed method is validated based on noise-free and noisy dent signals generated from elasto-plastic finite element analyses of a pipe segment subjected to an indenter and shown to be more effective than the commonly used wavelet transform-based hard- and soft-thresholding methods in terms of the root mean square error and the accuracy of the effective dent strain estimated from the denoised signal. The proposed method is further employed to denoise 42 real dent signals from in-service pipelines to illustrate its effectiveness and potential practical application to facilitate strain-based dent assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".