A Comparison of Wavelet Transform-Based and Fourier Transform-Based Denoising Methods for Strain-Based Pipeline Dent Assessments
Bibliographic record
Abstract
Abstract Dents are a common type of mechanical damage on buried steel pipelines resulting from external interference such as excavation activities near the pipeline right of way and rock impact. The strain-based dent assessment using dent signals obtained from inline caliper tools is commonly employed in practice to identify critical dents for mitigation. However, the strains evaluated using the dent signals are sensitive to noises contained in signal. Developing an effective denoising method is therefore crucial to the strain-based dent assessment. This paper compares the effectiveness of the wavelet transform- and Fourier transform-based denoising methods for dent signals. Three-dimensional elasto-plastic finite element analysis (FEA) is employed to simulate indentation scenarios on an unpressurized X60 pipeline segment with an outside diameter of 609.6 mm and a wall thickness of 7.6 mm. The FEA-simulated dent geometry is then employed to generate both noise-free and noisy dent signals by simulating the measuring process of a caliper tool with representative longitudinal and circumferential sampling resolutions. The noisy dent signals are obtained by adding Gaussian white noises to the noise-free signals. The wavelet transform- and Fourier transform-based denoising methods are applied to the generated noisy signals. The dent strains are evaluated using the denoised signals; furthermore, the true dent strains are evaluated using the noise-free signals. The effectiveness and practicality of the two denoising methods are evaluated and compared by comparing the root mean squared error and accuracy of the dent strains obtained from the denoised signals. Based on the analysis results, recommendations are provided regarding the suitable denoising method for practical strain-based dent assessment.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 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".