MONITORING OF PIPE-WALL THICKNESS AND ITS THINNING RATE BY ULTRASONIC TECHNIQUE
Bibliographic record
Abstract
An important issue in power generation and petrochemical industries is the monitoring of pipe wall thickness and corrosion/erosion rate. The pipes are usually subject to erosive and/or corrosive environments and any failure could be catastrophic. While periodic manual ultrasonic thickness measurement is the common practice in many industries, in certain cases, where higher accuracies are required, continuous monitoring systems are required. This paper introduces a measurement algorithm that can accurately measure the pipe wall thickness and estimate the pipe- wall thinning rate. The algorithm incorporates a model-based estimation technique for estimating the pipe wall thickness and thinning rate. It is an on-line non-intrusive ultrasonic thickness measurement tool for quick and accurate estimation of the erosion/corrosion rate and remaining pipe-wall thickness. The technique is applied to data measured from a pipe carrying high temperature liquid. The results show that the system can measure thinning rates as low as 10 µm/year within 5 days of data collection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".